AI-Assisted Development
Use AI tools to support SQL, Python and PySpark development while understanding, reviewing and validating every output.
AI-Driven Data Engineering
AI-Driven Data Engineering Career Program
A structured, industry-driven Data Engineering career program that builds strong fundamentals, production-level projects, responsible AI fluency and interview readiness in 5 to 5.5 months.
Overview
Not a syllabus. A preparation path.
You learn SQL, Python, PySpark, cloud, warehouse and orchestration tools through labs and integrated projects, while interview preparation runs alongside from week 1.
30%
Concepts
Strong foundations
50%
Hands-on
Labs and projects
20%
Career
Interviews and scenarios
AI-driven Data Engineering
WHY AI-DRIVEN DATA ENGINEERING MATTERS
Modern Data Engineers are expected to do more than build pipelines. They must understand how to use AI tools responsibly to accelerate engineering work, investigate failures, improve data quality, generate documentation and support better technical decisions—without compromising security, accuracy or engineering fundamentals.
AI and big data are consistently listed among the fastest-growing skills in the World Economic Forum’s Future of Jobs reporting, and LinkedIn’s workforce research describes AI as reshaping the skills required across most jobs. Employers increasingly value engineers who combine strong fundamentals with practical, responsible AI fluency.
Intelligent Data Quality
Learn how AI-assisted methods can support anomaly identification, data validation and the investigation of unexpected data behaviour.
Faster Troubleshooting
Use AI as an engineering assistant to analyse errors, understand logs, identify possible root causes and evaluate solutions.
Documentation and Testing
Use AI responsibly to support technical documentation, test-case creation, code explanation and workflow standardisation.
Monitoring and Production Support
Understand how AI can assist with incident analysis, pipeline monitoring, performance investigation and production decision-making.
Agentic AI Workflows
Understand the fundamentals of AI agents, tool-based workflows, human approval, safety controls and their practical applications in Data Engineering.
Traditional Data Engineer
- Understands Data Engineering tools
- Writes pipelines manually
- Performs repetitive investigation
- Depends heavily on manual documentation
- May use AI without proper validation
AI-Driven Data Engineer
- Has strong SQL, Python, PySpark and cloud fundamentals
- Uses AI to accelerate engineering work
- Validates AI-generated outputs before implementation
- Uses AI for debugging, documentation and testing
- Understands security, governance and responsible AI usage
- Combines engineering judgement with modern AI capabilities
AI does not replace engineering knowledge. It increases the value of engineers who have strong fundamentals, critical thinking and the ability to use AI responsibly.
Confidential company or client data must never be entered into public AI tools.
Ideal candidates
Who the program is built for
Fresh graduates
From technical or analytical backgrounds who want a structured start.
Non-IT graduates willing to work consistently
Commitment and daily practice matter more than your degree.
IT professionals seeking a Data Engineering transition
Move from support, testing or software roles into data.
ETL, BI, database, support and backend professionals
Extend existing skills to Spark, cloud and modern platforms.
Candidates who completed another course but lack confidence
Turn scattered knowledge into project evidence you can explain.
Working professionals seeking structured preparation
Smaller weekly tasks that fit around a full-time job.
No previous Data Engineering job title is mandatory. Basic computer understanding, commitment and consistent practice are necessary.
Learning outcomes
By the end of the program you will be able to
- Write maintainable, performant SQL and Python transformation code
- Model data for analytics and implement SCD2 history
- Design batch, incremental, API and streaming ingestion pipelines
- Build and tune distributed Spark jobs and read the Spark UI
- Deploy cloud data pipelines on AWS and Azure
- Run warehouse and lakehouse workloads on Snowflake and Databricks
- Orchestrate, test, monitor and document pipelines with Airflow and dbt
- Use AI assistants responsibly and prototype safe, read-only AI tooling
Structure
A focused 5 to 5.5-month journey
22 structured weeks and 240 guided live and lab hours — about 10 guided hours a week, plus 5 to 7 hours of self-practice.
Weekly rhythm
- Weekday live sessions and a weekend lab or review block
- Every lecture recorded; trainer connect for doubts after recordings
- Smaller weekly tasks that fit around a full-time job
- Regular progress reviews on assignments and projects
Admission process
- 1Career discussion
Understand your goals and availability. - 2Skill assessment
Identify your current SQL and Python level. - 3Learning-path review
Recommend suitable preparation. - 4Admission confirmation
Secure your seat in the limited batch.
22-week roadmap
Week by week
1Month 1 · Foundations: Linux, Git, SQL
Topics: DE fundamentals and lab setup; Linux navigation, permissions, pipes; Git basics
Milestone: Lab ready; first GitHub repo
2Month 2 · Modelling and Python engineering
3Month 3 · Big Data, PySpark and the start of cloud
4Month 4 · Cloud and warehouses
5Month 5 · Modern platform, orchestration and streaming
The exact weekly sequence may be adjusted based on batch progress and industry updates without reducing the overall learning outcomes.
Technology stack
What you will work with
- Linux
- Git and GitHub
- SQL
- Data Modelling
- Python
- NumPy
- Pandas
- APIs and ETL
- Hadoop
- Hive
- Data Quality
- PySpark
- Spark SQL
- AWS
- Azure
- GCP
- Snowflake
- Databricks
- Delta Lake
- Apache Airflow
- dbt
- Kafka
- Structured Streaming
- Docker
- CI/CD
- Monitoring and Observability
- Agentic AI for Data Engineering
- Generative AI
- Prompt Engineering
- Agentic AI
- AI-Assisted Development
- AI-Assisted Data Quality
- Responsible AI Practices
Students learn where AI can improve engineering productivity, where human judgement is essential and how to verify every AI-assisted output before using it in a production environment.
Production projects
Build Production-Level Projects Deeply
- Data Sources
- Data Ingestion
- Raw Storage
- Data Validation
- PySpark Transformation
- Data Quality
- Cloud Processing
- Warehouse Loading
- dbt Modelling
- Airflow Orchestration
- Monitoring
- Documentation
- Final Demonstration
What the learner builds
- Source-to-target architecture and data model
- Batch, incremental and API ingestion
- PySpark transformations and curated layers
- Snowflake models and analytics-ready outputs
- Idempotency, retries and safe reprocessing
- Data quality, reconciliation and alerting
- Logging, monitoring, cost and security controls
- Tests, documentation, Git workflow and CI/CD
What you deliver and submit
- Working pipeline from source to dashboard
- Architecture diagram and design decisions
- Test and quality evidence; failure-recovery demo
- Cost notes and runbook
- GitHub repository with README
- 15-minute demo and architecture viva
Students should be able to build, debug and explain your projects confidently during interviews.
Interview preparation track
Interview readiness runs alongside the course
Track 1
Core Technical Track
Weekday technology sessions, labs, assignments and project integration.
Track 2
Additional Career Track
- Every Saturday: Interview preparation
- Alternate Sundays: Mock interviews
- Technical question practice
- Scenario-based questions
- Project explanation
- HR round preparation
- Feedback and improvement plan
Weekend interview preparation and mocks are additional and do not reduce the 240-hour core syllabus.
Evaluation system
- Graded assignments and lab sets in every module (for example a graded SQL set and a 50-problem Python set)
- Checkpoints through the program, including a SQL assessment and a pipeline code review
- Skill-gap reviews at week 8, week 18 and week 22
- First full mock interview in week 18
- Final architecture review, implementation, testing, documentation and demo
Attendance, recordings and doubt support
- Live attendance is expected; recordings support learning but do not replace guidance.
- Missed a session? Watch the recording, practise, then connect with the trainer for doubts.
- Plan roughly 5 to 7 hours of self-practice each week on top of about 10 guided hours.
Career support
Preparation and assistance until your first offer
Resume, LinkedIn, GitHub, job-portal profiles, HR call handling and mock interviews. See Placement Support for the full process.
Placement outcomes depend on the learner’s consistency, performance, eligibility and market conditions. ITMentor provides preparation and placement assistance, not an unconditional job guarantee.
FAQ
Frequently asked questions
Is ITMentor a typical coaching institute?
No. ITMentor is a structured, industry-driven Data Engineering career preparation program. The focus is not on completing a syllabus but on helping you build, debug and explain real work, and prepare for interviews from week 1.
Who conducts the training?
Working IT professionals with 8+ years of real-world experience who actively use the technologies they teach. Mentor profiles are published on the Mentors page with their consent.
Can freshers join?
Yes. Fresh graduates from technical or analytical backgrounds can join. No previous Data Engineering job title is required — basic computer knowledge and the discipline to practise daily are what matter.
Can non-IT candidates join?
Yes, if you are willing to work consistently. Your career discussion and skill assessment will show whether you should start with extra preparation in SQL or Python.
What is the program duration?
5 to 5.5 months, structured over 22 weeks.
How many hours of training are included?
240 guided live and lab hours (about 10 guided hours a week), plus 5 to 7 hours of self-practice each week. Weekend interview preparation and mocks are additional.
Are sessions live or recorded?
Both. Sessions are live (online, with a classroom option in Chhatrapati Sambhajinagar), supervised labs are included, and every lecture is recorded. Recordings support learning; they do not replace guidance.
Are the production-level projects individual or guided?
They are guided. You build integrated production-level projects and real-world use cases step by step under mentor supervision, and you present them in a demo and architecture viva. Please speak with our career team for the latest details on how work is organised in your batch.
When does interview preparation begin?
From week 1, alongside the technical sessions — not after the course ends.
How frequently are mock interviews conducted?
Interview preparation is held every Saturday and mock interviews on alternate Sundays, with written feedback and a fix list after each round.
Is placement guaranteed?
No. ITMentor provides preparation and placement assistance, not an unconditional job guarantee. Outcomes depend on your consistency, performance, eligibility and market conditions.
What does placement assistance include?
Skill assessment, gap identification, project readiness review, resume, LinkedIn and job-portal optimisation, job-search strategy, HR call handling, mock interviews, offer evaluation and post-placement guidance — until your first offer.
Will I receive support after placement?
Yes. We guide you on understanding your projects, workplace communication and settling into your first role.
What equipment is required?
A laptop and a stable internet connection are expected for labs. Please speak with our career team for the latest details.
Are cloud lab costs included?
Please speak with our career team for the latest details.
Can working professionals manage the schedule?
The program uses weekday live sessions and a weekend lab or review block, with smaller weekly tasks designed to fit around a full-time job. Plan for roughly 5 to 7 hours of self-practice each week.
What happens if I miss a lecture?
Every lecture is recorded. Watch it, practise, and connect with the trainer for doubts.
How can I verify student success stories?
Where a student chooses to share their own LinkedIn profile or testimonial post, we link to it with the story. A Verified label appears only when supporting proof is on file with ITMentor. Employer names, packages and photographs are published only with the student’s consent.
How do I book a career discussion?
Use the form on the Contact page, WhatsApp us, or call 93074 31763 / 97300 96184.
Is EMI available?
Please speak with our career team for the latest details.