CAREER DESTINATION SIGNAL250+companies across learner outcomes
NEXT LIVE BATCH · STARTING SOON · ONLY 30 LEARNERS
Build Cloud Data Engineering Skills Employers Can Verify
Learn Azure data engineering through live classes, guided labs and 12 hands-on projects. Prepare for DP-900, DP-700 and Databricks certification while building a recruiter-ready portfolio.
DP-900Microsoft Azure Data Fundamentals
DP-700Microsoft Fabric Data Engineer Associate
DBXDatabricks Certified Data Engineer Associate
500+ Learners Trained92% Placed250+ Companies₹46 LPA Highest CTC₹17 LPA Average CTCUp to 150% Salary Hike4.9/5 Learner Rating
Tue · Thu · Sat8:00 PM–10:00 PM IST3-Year AccessEMI AvailablePlacement Support Till Placed*Dedicated LMSTechnical PlaygroundCompletion Certificate
OUR STUDENTS HAVE BEEN HIRED ACROSS 250+ COMPANIES
Career Pipeline Control Room● COHORT SYSTEM ONLINE
12Capstone Projects
15Mock Interviews
3 YearsLearning Access
₹17 LPAAverage CTC
6Résumé Sessions
250+Career Destinations
Learner outcomesReview definitions and eligibility →
01
0
Learners trained
02
0
Placement success rate
03
0
Highest learner CTC
04
0
Highest salary hike
05
0
Learner rating
06
0
Companies where students were hired
Career destination network
A growing learner network across 250+ companies
Explore selected career destinations across consulting, technology, analytics, fintech, automotive, cloud and product engineering.
500+
LEARNER COMMUNITYLearners trainedLive, project-led learning
92%
CAREER PROGRESSPlacedAcross multiple role families
SELECTED DESTINATIONMercedes-BenzAutomotive & technology
Why BitsnBugs
Because career change needs more than course content
BitsnBugs combines live technical instruction, repeated implementation, portfolio proof, interview rehearsal and structured placement execution in one accountable learning system.
Career-first learning architecture
Learn in the sequence employers evaluate: foundations, pipelines, Lakehouse engineering, production reasoning, portfolio proof and interview defence.
One connected path—not disconnected tool tutorials.Structured roadmapIndustry sequenceInterview flow
Live practitioner-led guidance
Work through concepts, architecture decisions, debugging and implementation trade-offs in live classes with a maximum of 30 learners.
More interaction, context and accountability.Live classesMentor supportCohort accountability
Twelve capstones that create proof
Build across batch, streaming, migration, Lakehouse, data quality, security, governance, analytics, observability, FinOps and deployment scenarios.
Code, READMEs, diagrams and interview narratives.GitHub proofArchitecture docsProject defence
Review—not passive completion
Use quizzes, labs, assignments and project checkpoints to find technical gaps early and improve engineering judgement.
Feedback is tied to visible deliverables.QuizzesLabsAssignment review
Six résumé transformation sessions
Convert responsibilities and project work into concise, role-aligned evidence that recruiters and ATS systems can understand.
Position the value of your work—not a list of tools.ATS-ready résuméLinkedIn polishPositioning support
Fifteen technical mock interviews
Practise SQL, PySpark, Azure services, architecture scenarios, project defence and communication under interview conditions.
Repetition turns knowledge into confident execution.SQL + PySparkAzure scenariosCommunication practice
Three-year learning runway
Return to recordings, resources, labs and project material for revision, interview preparation, portfolio improvement and continuing practice after completion.
Learning access continues beyond the live cohort.RecordingsRevision libraryPortfolio upgrades
Placement execution till placed*
Combine opportunity guidance, application discipline, profile positioning, interview feedback and follow-through support.
A structured job-search operating system.Opportunity guidanceFeedback loopsSupport till placed*
30maximum learners per batch
12capstone projects
6résumé sessions
15mock interviews
3 yearspost-completion access
250+companies
Compare the learning model
See what changes when learning, proof and placement execution are designed together
The comparison below is about the operating model—not a claim that every alternative course has the same features.
SELECTED LEARNING MODEL
BitsnBugs accelerator
A connected system that moves from live instruction to cloud implementation, 12 capstones, GitHub evidence, interview repetition and placement support.
Structure95%
Portfolio proof95%
Interview practice90%
Career execution92%
Best for learners who need a complete skill-to-portfolio-to-interview operating system—not another video library.
What mattersRandom tutorialsRecorded courseBitsnBugs
Role-aligned learning roadmapSelf-plannedPredefined videosPersonalised pathway
Live instructor guidanceRarelyUsually absentIncluded
Real-time doubt resolutionCommunity dependentTicket or forumLive + mentor support
Quizzes and knowledge checksSelf-createdVariesIncluded
Guided Azure cloud labsSelf-managedDemonstration onlyIncluded
Assignments with reviewNo reviewAutomated or limitedApplied review
Twelve capstone projectsUnstructuredUsually 1–3Twelve capstones
Architecture decision practiceRarelyConceptualBuilt into projects
Testing, monitoring and recoveryOften skippedLimitedProduction-focused
GitHub portfolio buildingSelf-managedUsually absentGuided
Project READMEs and diagramsSelf-createdUsually absentIncluded
Résumé transformationNoGeneric templatesSix sessions
LinkedIn positioningNoBasic guidanceRole-focused
Technical mock interviewsNoFew or noneFifteen sessions
Project-defence practiceNoRarelyRepeated practice
Job-search operating planNoGeneric adviceStructured execution
Placement supportNoUsually time-limitedTill placed*
Learning access after completionPlatform dependentOften 6–12 monthsThree years
Dedicated LMS accessScattered bookmarksBasic video portalIntegrated 3-year LMS
Technical problem-solving playgroundExternal websitesUsually absentDedicated practice environment
Detailed interview guidesSelf-researchedGeneric notesRole and topic-specific guides
Downloadable cheatsheetsSelf-createdVariesCurated revision toolkit
Curated technical question bankUnstructured searchLimited quizzesProgressive question bank
Code and architecture reviewNo reviewLimited feedbackEngineering-focused review
Readiness and progress trackingSelf-trackedCompletion percentagePortfolio + interview readiness
Cloud cost and cleanup guidanceTrial and errorOften omittedBuilt into guided labs
Application and follow-up trackerSelf-managedUsually absentStructured job-search toolkit
*Placement support till placed is subject to the published learner-participation and eligibility policy. Employment, employer selection and compensation are not guaranteed.
Trust before transaction
Verify the programme before you commit
High-converting education pages should reduce uncertainty with evidence, clear definitions and visible operating policies—not louder promises.
Public mentor verification
Named tutors have public LinkedIn profiles and clearly stated teaching responsibilities.
Profiles linked on-pageOutcome methodology
Placement, CTC, salary-hike and rating claims are labelled as reported outcomes and require a published measurement note.
No outcome is presented as a guaranteeCompany-logo disclosure
Destination logos describe reported learner employment outcomes—not formal hiring partnerships or endorsements.
Relationship wording stays preciseSmall-cohort accountability
Each batch is capped at 30 learners to support live interaction, review and mentor visibility.
Capacity is fixed, not simulatedEvidence-led completion
Learning is tied to quizzes, labs, assignments, code reviews, 12 capstones, READMEs and architecture artefacts.
Progress creates inspectable proofTransparent enrolment terms
GST treatment, EMI terms, refund policy, access conditions and placement eligibility should be visible before checkout.
No hidden decision-critical termsOUTCOME EVIDENCE CHAINSource record → eligibility definition → measurement period → aggregate metric → learner disclosure
✓ Publish the learner-count period✓ Define “placed” and eligible learners✓ State salary-hike sample and method✓ Link refund and placement policies✓ Obtain logo and testimonial permissions✓ Keep company associations accurately worded
Who this is for
Built for professionals who need a modern, credible next move
The programme supports skill modernisation, career re-entry and serious transition. Each path starts differently, but every learner must practise, build and explain real work.
Laid-Off Professionals
Restart with current cloud skills, recent project evidence and structured interview re-entry preparation.
Built into the learning pathCareer-Gap Returners
Rebuild a recent technical track record and learn how to present the gap without allowing it to define your profile.
Built into the learning pathSQL DBAs & ETL Developers
Move from stored procedures, SSIS and on-premise data platforms to modern Azure and Lakehouse workflows.
Built into the learning pathData Analysts & BI Engineers
Move upstream from reporting into ingestion, transformation, orchestration and data-platform architecture.
Built into the learning pathSoftware Developers & QA
Use your technical base to enter distributed processing, cloud orchestration and production data engineering.
Built into the learning pathSupport & Cloud Operations
Translate troubleshooting and platform knowledge into ownership of pipelines, monitoring and reliability.
Built into the learning pathGraduates & Early-Career Learners
Develop foundations and portfolio evidence before competing for entry-level data-engineering roles.
Built into the learning pathNon-Technical Career Switchers
Build fundamentals in sequence with additional practice, screening and realistic expectations.
Built into the learning pathCareer restart studio
A layoff or career gap is context—not the conclusion of your career story
A strong return-to-work profile combines transferable experience, current technical proof and a concise explanation of the transition. The programme helps you build all three.
RESTART PATH ACTIVE
Re-enter with current proof
Translate your existing experience into cloud-data relevance, add recent project evidence and rehearse a concise layoff narrative for interviews.
01Map transferable experience
02Build current Azure evidence
03Reframe résumé and LinkedIn
04Practise re-entry interviews
VISIBLE OUTPUTRecent GitHub projects + role-aligned professional story
Interactive course-fit diagnostic
Map your background to the fastest credible route into cloud data engineering
Use the diagnostic to identify the modules, portfolio outputs and career-support activities most relevant to your starting point. It is a pathway guide, not an outcome promise.
Your recommended emphasis
Cloud foundations → guided labs → portfolio evidence → interview practice
Select your situation and obstacle to personalise this path.
The weekly execution system
Every week moves from concept to implementation to interview proof
A predictable learning rhythm reduces passive consumption and makes progress visible. Select a stage to see the learner output.
ACTIVE WEEKLY STAGE
STAGE 01 · LIVE LEARNING
Understand the system before using the tool
Connect business requirements to architecture, service selection, data flow, security, cost and operational constraints.
OUTPUTConcept map + architecture notes + questions to test
Learning engine
Every learning activity must produce a measurable output
The engine combines instruction, retrieval practice, implementation, review, documentation and interview rehearsal. Learners do not progress only because a video was watched.
12
Capstone projects
Core and advanced builds across streaming, migration, Lakehouse, quality, governance, security, observability, analytics, FinOps and deployment.
OUTPUT · repositories + architecture packsQuizzes
Frequent retrieval checks for Python, SQL, Spark, Azure services and architecture decisions.
OUTPUT · gap reportGuided cloud labs
Stepwise implementation with cost controls, cleanup instructions and troubleshooting prompts.
OUTPUT · execution evidenceApplied assignments
Independent tasks that test whether the learner can adapt—not merely copy—the demonstrated workflow.
OUTPUT · reviewed submissionCode and pipeline review
Naming, modularity, error handling, performance, security and operational readiness are reviewed.
OUTPUT · correction planGitHub publishing
Clean repositories, meaningful commits, READMEs, diagrams, setup steps and known limitations.
OUTPUT · recruiter-readable proofData-quality gates
Validation rules, quarantine patterns, reconciliation and audit evidence are built into projects.
OUTPUT · quality scorecardMentor office hours
Focused doubt resolution for blockers, architecture choices, debugging and project decisions.
OUTPUT · unblock planProject defence drills
Learners explain the business problem, architecture, trade-offs, failures and optimisation choices.
OUTPUT · interview storyPeer demonstrations
Short project walkthroughs develop clarity, ownership and the ability to answer follow-up questions.
OUTPUT · demo feedbackCareer checkpoints
Portfolio, résumé, LinkedIn and application readiness are assessed at defined milestones.
OUTPUT · readiness score3Y
Post-completion learning access
Three years of access for revision, portfolio improvement, certification preparation and interview reinforcement.
OUTPUT · continued learning runwayLearnTestBuildReviewPublishDefendApply
Everything included beyond live classes
A complete learner-support toolkit inside one programme
The course combines structured learning, deliberate practice, project proof, interview preparation and job-search execution. Select a benefit to see the learner outcome.
SELECTED BENEFIT
Dedicated LMS access
Use one organised learning hub for live-session recordings, modules, resources, quizzes, submissions, progress and programme updates.
LEARNER OUTCOMEThree-year structured access after completion.
LearnPractiseBuildReviewPrepareApply
Technology command centre
Learn the Azure data stack as one connected engineering system
Explore each technology by business purpose, architecture position, practical build, production concern and interview relevance.
Technology 01
Python
Automation, file processing and reusable data-engineering logic.
WHAT YOU BUILD
Build ingestion utilities, validation helpers and notebook workflows.
Architecture fit · Interview application · Hands-on implementation
Twelve capstone projects
Build a portfolio with range, depth and production judgement
Twelve projects create repeated evidence across ingestion, streaming, migration, modelling, quality, security, observability, FinOps, analytics and deployment. Each produces a repository, architecture pack, operational evidence and interview narrative.
Your completion portfolio
Finish with a professional evidence system—not a certificate alone
Every artefact—from LMS progress and technical practice to GitHub projects and interview feedback—is designed to help mentors review your work, recruiters scan your capabilities and interviewers test your ownership.
Recruiter-readable GitHub portfolio
Versioned repositories with clean READMEs, code structure, architecture diagrams, setup instructions, decisions, limitations and interview talking points.
Clean READMEsArchitecture diagramsInterview-ready links
PORTFOLIO REPOSITORYazure-streaming-lakehouseREADME · architecture.svg · notebooks · tests · runbook
Capstone projects
Portfolio range across batch, streaming, migration, quality, governance, analytics, observability, FinOps, security and deployment.
Batch + streamingGovernance + qualityDeployment + FinOps
Résumé sessions
Role targeting, evidence selection, quantified impact statements, ATS structure and recruiter readability.
ATS optimisationQuantified impactRole targeting
Mock interviews
SQL, PySpark, Azure, system design, project defence, behavioural communication and feedback tracking.
Technical depthArchitecture defenceFeedback reports
Architecture portfolioContext, diagrams, service selection and trade-offs
Data-quality scorecardsRules, failures, reconciliation and audit evidence
Operational runbooksMonitoring, recovery, cost controls and support notes
Project demo scriptsFive-minute and fifteen-minute walkthrough formats
Interview story bankProblem, ownership, decisions, failures and outcomes
LinkedIn positioningHeadline, skills, projects, proof links and recruiter discovery
Application trackerTarget companies, role fit, status and follow-up workflow
Certification study planDP-700 and Databricks-aligned revision map
LMS learning recordCompleted modules, quizzes, lab submissions and progress history
Technical practice recordSolved SQL, Python, PySpark and architecture question sets
Interview guide libraryTopic-wise revision plans and scenario-answer frameworks
Personal cheatsheet packSyntax, patterns, service comparisons and interview checklists
Mock feedback reportsRepeated strengths, gaps and improvement actions
Cloud-cost checklistBudgets, shutdown steps and safe lab operating practices
Post-completion access
Continue revision, project upgrades, certification preparation and interview practice after the live programme.
3-year revisionCertification prepInterview refresh
Readiness dashboard
Track project completion, review status, mock-interview themes, portfolio quality and application readiness.
Progress trackingGap visibilityNext-step clarity
Role-targeting map
Match your evidence to Azure Data Engineer, Lakehouse Engineer, Fabric Data Engineer and related role families.
Azure rolesFabric rolesLakehouse roles
What a stronger profile demonstrates
Hiring teams need more than a list of tools
The portfolio is structured to help you demonstrate how you think, build, test, recover and communicate—not merely which software names you recognise.
Architecture judgement
Explain why a service, storage pattern or orchestration approach fits the requirement.
Production thinking
Discuss monitoring, retries, data quality, security, cost, failure recovery and operational ownership.
Implementation evidence
Show code, pipeline configuration, project structure, diagrams and documented decisions.
Scale awareness
Reason about partitions, shuffles, file sizes, incremental processing and performance trade-offs.
Business communication
Translate technical choices into reliability, speed, governance, cost and analytical value.
Interview readiness
Defend the project under follow-up questions instead of repeating a memorised description.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Learning mode
Live instruction · Guided lab · Quiz · Assignment · Review
Evidence produced
Implementation output, architecture notes and interview talking points.
Live schedule and access
Built around the reality of working professionals
Evening live classes, limited batch size, continuing access and structured practice.
Next batch starting soon
Three live learning touchpoints every week
TUESDAYLive Class
THURSDAYLive Class
SATURDAYLive Class
Smaller cohort cap for interaction and review.
Revision and interview-preparation access.
Provider and tenure details to be published.
Labs, assignments and doubt resolution.
Structured professional positioning and recruiter-readability support.
Technical, project-defence and communication practice under feedback.
BitsnBugs certification
A simple, professional certificate that complements your portfolio
Preview the certificate, see what it recognises and explore the certification-preparation support included in the programme.
CERTIFICATE OF COMPLETION
Azure Data Engineering Career Accelerator
This certifies that
Sample Learner Name
has successfully completed the instructor-led programme and its required learning activities and capstone work.
Python · Advanced SQL · PySpark · ADF · Databricks · Delta Lake · Microsoft Fabric
Programme CompletionBitsnBugs
BITSNBUGS
CERTIFIED
CERTIFIED
Credential IDBNB-ADE-SAMPLE-001
Programme completion
Recognises completion of the structured Azure Data Engineering learning path.
Capstone execution
Complements twelve capstone projects, GitHub repositories and architecture documentation.
Technical practice
Supports a learning record built through quizzes, labs, assignments and review checkpoints.
Career preparation
Pairs with résumé sessions, interview guides, mock interviews and portfolio positioning.
Azure Data Fundamentals (DP-900)
Build a clear foundation in core data concepts, relational and non-relational workloads, analytics workloads and Azure data services.
Data fundamentalsAzure servicesPractice questions
Fabric Data Engineer Associate (DP-700)
Structured revision and practical preparation around Fabric data-engineering concepts and project implementation.
Study roadmapPractice questionsRevision guides
Databricks Certified Data Engineer Associate
Preparation supported by Lakehouse projects, PySpark practice, Delta Lake workflows and technical question banks.
Concept reviewLakehouse practiceMock questions
Placement accelerator
Placement support is a structured career-execution system
The programme combines positioning, portfolio proof, repeated interview practice and opportunity execution. Support continues till placed, subject to the published participation policy.
SELECTED REPORTED CAREER DESTINATIONSPlacement preparation is built around evidence, interview repetition and disciplined execution.
01
Career Diagnosis
Map experience, gaps, target roles and skill evidence.
02
Portfolio Proof
Twelve capstones, GitHub, diagrams and project narratives.
03
Résumé Transformation
Six sessions to sharpen role fit and measurable evidence.
04
LinkedIn Positioning
Headline, skills, projects and recruiter discovery.
05
Mock Interviews
Fifteen technical, architecture and behavioural practices.
06
Opportunity Support
Continues till placed, subject to published eligibility terms.
Structure, feedback and opportunity support
- Six résumé sessions and LinkedIn positioning
- Fifteen mock interviews with feedback
- Project and portfolio review
- Role targeting and application guidance
- Opportunity and referral support where available
- Support till placed under published eligibility terms
Completion, practice and professional follow-through
- Attend or catch up on required learning
- Complete the required capstones and assignments
- Apply interview feedback
- Maintain a consistent application cadence
- Respond promptly to opportunities
- Keep profile, portfolio and availability current
92%Reported placement rate
46 LPAHighest CTC
150%Salary hike
Till PlacedPlacement-support duration
Placement credibility
Placement support becomes credible when both sides have defined responsibilities
Use the readiness check to understand the active participation expected from learners. Support is strongest when portfolio completion, interview practice and application follow-through are measurable.
92%placement rate
₹46 LPAhighest CTC
150%salary hike
PLACEMENT READINESS CHECK0%
Select the actions you are prepared to complete.
Start by selecting the commitments you can make.
Tutors and mentors
Learn with practitioners and specialist mentors at every stage
Explore the tutor roster for technical learning, project execution, certification preparation, interview readiness and career positioning.
MENTOR ROSTER
01 / 03
Swipe, drag or use the controls. The centred card is the active mentor track.
Arihant Jain
Lead Data Engineer (Tiger Analytics)
Guides Azure Lakehouse architecture, Databricks implementation, PySpark engineering, code review and capstone defence.
View LinkedIn profile ↗01
Pavan Kalal
Consultant Data Engineer (Deloitte)
Supports project execution, learning discipline, résumé positioning, interview preparation and professional delivery habits.
View LinkedIn profile ↗02
Muskan Goel
Lead AI Engineer (PwC)
Connects modern AI engineering, data workflows and responsible solution design with practical project and career guidance.
View LinkedIn profile ↗03TeachPractical concepts and architecture.
ReviewCode, projects and decisions.
ChallengeScenarios that test depth.
PrepareCertification, interviews and careers.
Customer reviews
Real journeys, floating in motion
A living wall of learner stories with room for photographs, outcomes and approved feedback. Hover anywhere on the story wall to stop the motion.
LEARNER REVIEW · PROJECT-LED LEARNING
Mohit KumarAzure Data Engineering Learner
“The programme gave me a clear path across SQL, PySpark, Azure Data Factory and Databricks. The guided labs, capstone projects and mock interviews helped me practise consistently and explain my work with greater confidence.”
SQL and PythonAzure project confidence
Guided labs · Capstone projects · Mock interviews
Learner review
LEARNER REVIEW · CLOUD DATA FOUNDATIONS
IshanaData Engineering Learner
“The live sessions made Azure and Databricks easier to understand. Guided practice and project reviews helped me improve each implementation step by step without feeling overwhelmed.”
Cloud foundationsProject confidence
Live classes · Guided labs · Project feedback
Learner review
LEARNER REVIEW · HANDS-ON PRACTICE
RakeshCloud Data Engineering Learner
“The structured sequence helped me connect Python, SQL and Azure services instead of learning them separately. Regular practice and mentor feedback made the technical concepts much easier to apply.”
Disconnected conceptsConnected cloud skills
Structured learning · Mentor feedback · Azure practice
Learner review
LEARNER REVIEW · PORTFOLIO DEVELOPMENT
RohitAzure Data Engineering Learner
“Building projects and documenting the architecture gave me a clearer understanding of how data pipelines work. I now have stronger examples to discuss during technical interviews.”
Theory knowledgePortfolio evidence
Architecture practice · GitHub portfolio · Interview stories
Learner review
LEARNER REVIEW · INTERVIEW PREPARATION
RihanData Engineering Learner
“The combination of labs, assignments and mock interviews helped me identify gaps quickly. The feedback was practical and gave me a better plan for improving both technical depth and communication.”
Learning gapsInterview readiness
Assignments · Mock interviews · Actionable feedback
Learner review
LEARNER REVIEW · CAREER-READY PRACTICE
RajithaAzure Data Engineering Learner
“The programme helped me organise my learning around practical Azure data-engineering workflows. Guided labs, project reviews and interview practice made it easier to understand the architecture and explain my decisions clearly.”
Guided learningCareer-ready confidence
Azure workflows · Project reviews · Interview practice
Learner review
Hover anywhere to stopSwipe or drag on mobile
Decision confidence
Resolve the practical questions before you enrol
Conversion improves when the programme makes the workload, support model, cost controls and learner responsibilities explicit.
“I work full time.”
Live classes run Tuesday, Thursday and Saturday from 8:00 PM to 10:00 PM IST, with three-year post-completion access for revision.
“I am worried about cloud cost.”
Labs should use free credits where available, controlled resources, budgets, shutdown routines and cost-monitoring practices.
“I have a career gap or layoff.”
The programme focuses on current proof, transferable experience, résumé positioning and repeated re-entry interview practice.
“I am not confident in interviews.”
Fifteen mock interviews cover SQL, PySpark, Azure services, architecture, projects and behavioural communication.
“I have no portfolio.”
Twelve capstones are converted into GitHub repositories, diagrams, READMEs, implementation notes and interview narratives.
“What happens after the course?”
You retain three-year access and receive placement support till placed, subject to the published eligibility and participation policy.
Salary-impact visualiser
See what a 150% increase means mathematically
Move the slider to visualise the reported 150% salary-improvement metric. This is an illustration, not an earnings forecast or guarantee.
Current CTC₹6.0L
Illustrative +150%₹15.0L
Actual outcomes depend on experience, previous compensation, role fit, location, interview performance and market conditions.
Pricing
A complete skill, portfolio and placement-preparation system for ₹24,999
The programme combines live instruction, dedicated LMS access, a technical practice playground, 12 projects, portfolio building, interview preparation and continued placement support.
Programme value
₹1,80,000
Combined value of the complete learning, portfolio and career-support system
Instructor-led Azure Data Engineering programme₹50,000
Twelve capstone projects and engineering review₹35,000
Guided cloud labs, quizzes and assignments₹15,000
Dedicated LMS, playground, guides and cheatsheets₹15,000
GitHub portfolio, diagrams and documentation₹15,000
Six résumé sessions and LinkedIn positioning₹15,000
Fifteen mock interviews and dedicated interview preparation₹20,000
Placement support till placed₹15,000
Combined Programme Value₹1,80,000
Questions
Clear answers to the questions that affect your decision
Programme scope, workload, placement support, reported outcomes, payment and certification are explained without converting them into guarantees.
Yes. The programme is designed to help experienced professionals refresh their stack, create recent evidence and practise explaining their experience in current data-engineering interviews. Placement support is not an employment guarantee.
Yes. Career-gap returners receive the same technical roadmap, portfolio building and interview preparation. The focus is on showing current capability through recent work and a clear return-to-work narrative.
Prior programming helps but is not mandatory. Python and SQL foundations are included. Complete beginners and non-technical switchers should expect additional weekly practice and may be advised to complete a readiness assessment.
The page presents the figure supplied by BitsnBugs. Before public launch, the methodology should state the cohort period, sample size, eligibility criteria, whether internal transitions are included and the date the metric was last updated.
No. It is a reported outcome metric, not a promise. Salary outcomes vary with previous experience, current compensation, interview performance, location, role fit and market conditions.
The programme includes résumé and LinkedIn work, 15 mock interviews, opportunity guidance and placement support that continues until placement, subject to the published participation and eligibility terms. Employment is not guaranteed.
Six résumé-focused sessions are included. The final operating policy should specify whether all six are one-to-one, group-based or a combination.
The programme includes 12 capstone projects, supported by quizzes, guided labs, assignments, GitHub documentation and portfolio-building activities.
Live classes are scheduled on Tuesday, Thursday and Saturday from 8:00 PM to 10:00 PM IST. The next batch is starting soon and each batch is capped at 30 learners.
Learners receive three years of access after programme completion, including the learning portal and available course resources, subject to the final access policy.
Yes. EMI is available. The payment page should publish eligible cards or providers, tenure options and any interest or processing charges.
Confirm whether ₹24,999 is the final tax-inclusive amount or whether GST is added at checkout before publishing.
Yes. The learning system includes quizzes, guided cloud labs and applied assignments alongside live instruction. The operating plan should specify assessment frequency, review turnaround and completion requirements.
Yes. Project work is structured for GitHub documentation with code, READMEs, architecture diagrams, implementation decisions and interview talking points. Final public-sharing rules should be confirmed for datasets and proprietary materials.
No exam fee is stated as included. The programme can support preparation for Microsoft Fabric Data Engineer Associate (DP-700) and Databricks Data Engineer Associate. Official examinations are independently administered.
The programme is taught and mentored by Pavan Kalal, a Consultant at Deloitte USI, and Arihant Jain, a Senior Data Engineer at Tiger Analytics with 4+ years of cloud data-engineering experience. Arihant’s public profile highlights Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, Unity Catalog, data governance, ETL/ELT, data quality and monitoring. Both public profiles are linked in the Tutors and Mentors section.
The exact refund window, eligibility conditions and request process must be linked from the pricing card before checkout is enabled.
If ₹24,999 is inclusive of GST, it is the final tax-inclusive course price. If GST is applicable, tax is added at checkout. The current preview keeps this status unconfirmed rather than publishing an unsupported tax statement.
Each batch is capped at a maximum of 30 learners. Live sessions are supported by guided labs, quizzes, assignments, project reviews and interview preparation. Confirm mentor-response and doubt-resolution service levels before launch.
Yes. Email hello@bitsnbugs.com, call +91-7617612992, or use the WhatsApp advisor link on the page. Publish support hours and the expected response time before launch.
A broader project set gives learners repeated practice across ingestion, batch and streaming processing, Lakehouse design, data quality, security, governance, analytics and deployment. The objective is depth through repetition—not twelve superficial demos.
Plan for approximately 6–8 hours each week in addition to live classes when projects are active. Beginners, career switchers and learners who need to rebuild Python or SQL foundations may require more time.
The programme is positioned as guided portfolio building. Before launch, publish the exact review format, review frequency, turnaround time and project-completion standard.
Three-year access supports catch-up and revision. The final page should state the recording publication timeline, doubt-resolution method and any attendance requirements for placement eligibility.
No. The count refers to companies represented in learner-reported employment outcomes. Company logos do not imply a formal hiring partnership or endorsement unless a separate written partnership exists.
No. Microsoft retired the Azure Data Engineer Associate certification and DP-203 exam on 31 March 2025. The current Microsoft data-engineering pathway referenced here is Fabric Data Engineer Associate, based on exam DP-700.
Experienced SQL, ETL, analytics or software professionals may be ready to enrol directly. Complete beginners, non-technical switchers and learners returning after a long gap should complete a fit conversation to confirm workload and foundation requirements.
Confirm the GST treatment, EMI terms, refund policy, access conditions, placement eligibility rules, instructor profiles, next batch date and the final checkout destination.
The LMS brings together available recordings, curriculum modules, downloadable resources, quizzes, assignment submissions, project materials, progress tracking and programme updates. Learners receive three years of access after programme completion, subject to the final access policy.
It is a dedicated practice environment for SQL, Python, PySpark, data modelling, Azure concepts and scenario-based technical questions. The objective is to improve problem-solving speed, accuracy and the ability to explain the reasoning behind an answer.
Learners receive dedicated interview preparation, detailed topic-wise guides, curated technical question banks, downloadable cheatsheets, project-defence frameworks and feedback from 15 mock interviews.
Yes. The resource toolkit includes concise references for syntax, architecture patterns, service comparisons, troubleshooting, interview revision and project documentation. Final file formats and update frequency should be stated in the LMS policy.
Email hello@bitsnbugs.com. For WhatsApp or telephone support, use +91-7617612992. The website provides direct mail, call and WhatsApp actions.
Review before you enrol
Review the complete programme before making the decision
Download the detailed curriculum, project architecture map, weekly learning system, class schedule, portfolio outputs and placement-support workflow.
9 curriculum phases12 capstone projectsTechnology architectureCareer-support workflow
Next-batch decision desk
A focused evening schedule for a measurable career build
Join a maximum-30 cohort with three live learning touchpoints each week, structured practice, 12 capstones and three years of access after completion.
Tuesday · Thursday · Saturday8:00 PM–10:00 PM ISTMaximum 30 learnersEMI available
ENROLMENT₹24,999GST treatment to be confirmedReview Full Value StackTalk to an Advisor
ADMISSIONS SUPPORThello@bitsnbugs.com+91-7617612992
Move from learning tools to proving you can build with them
Join the next live batch and build technical evidence, project depth, interview repetition and professional positioning—with a dedicated LMS, technical playground, detailed guides and three years of learning access.
