Program Highlights Engineered for Career Breakthroughs
A high-intensity, career-first learning curriculum developed alongside industry architects to deliver tangible production competence, verified credentials, and guaranteed corporate experience.
6 Months Instructor-Led
Live interactive classes led by Senior Engineers & Tech Leads from top product firms. 150+ hours of live instruction, recorded sessions, and dedicated weekly 1-on-1 doubt clearing.
IBM Learning Ecosystem Access
Authorized access to the IBM SkillsBuild enterprise learning portal, official cloud tools, and verified IBM credentials that authenticate your skills to global employers.
Microsoft Learning Ecosystem Access
Direct access to Microsoft Learn resources, real cloud sandbox architectures, and scenario-based hands-on exercises aligned with industry certification standards.
Projects: Hands-on Portfolio
Build 10+ production-grade end-to-end applications deployed to GitHub and cloud infra. Create a public portfolio of real architectures that impresses technical interviewers.
Credentials: Credential Pathways
Receive verifiable digital badges and accredited certificates recognized across 500+ recruitment partners. Directly shareable on LinkedIn, resumes, and candidate profiles.
Internship*: 6-Month Paid Corporate Internship*
Guaranteed entry into real corporate project teams upon completing capstones. Earn an assured monthly stipend along with an official corporate experience letter.
What You Will Learn
Comprehensive curriculum engineered by Staff Engineers to bridge theoretical concepts and production deployment.
Architect scalable relational models and analytical warehouses for petabyte-scale data.
- ✓ Dimensional Modeling: Kimball methodology, Fact and Dimension tables, Slowly Changing Dimensions (SCD 1, 2, 3)
- ✓ Data Warehousing Platforms: Snowflake architecture (virtual warehouses, clustering) and Google BigQuery
- ✓ Advanced SQL: Window functions, recursive queries, materialization, and query execution plan tuning
- ✓ Data Governance & Compliance: Role-based masking, data cataloging, and column-level encryption
Execute parallel processing and transformations across large-scale distributed clusters.
- ✓ Spark Internals: Driver, Executors, RDDs, DataFrames, Spark SQL, and Catalyst Optimizer
- ✓ PySpark Engineering: UDFs, caching, persistence, partitioned writes, and shuffle optimization
- ✓ Handling Data Skew: Salting techniques, broadcast joins, and dynamic resource allocation
- ✓ Lakehouse Technologies: Delta Lake, Apache Iceberg, ACID transactions, and time travel querying
Orchestrate complex DAG dependencies and maintain modular, version-controlled transformations.
- ✓ Apache Airflow Architecture: Schedulers, Webserver, Celery/Kubernetes Executors, and Workers
- ✓ Authoring DAGs: Sensors, Operators, Hooks, dynamic task mapping, and automated backfilling
- ✓ data build tool (dbt): Modularity, staging/marts layers, testing assertions, and documentation generation
- ✓ CI/CD for Data Pipelines: Automated linting with sqlfluff and git-driven pipeline deployment
Ingest and process continuous streaming data streams with sub-second latency.
- ✓ Kafka Core: Topics, Partitions, Brokers, Producer/Consumer groups, and Zookeeper vs KRaft
- ✓ Kafka Connect: Source and Sink connectors for zero-code ingestion into databases and data lakes
- ✓ Schema Management: Confluent Schema Registry and Avro serialization for schema evolution
- ✓ Stream Processing: Kafka Streams, ksqlDB, event-time processing, and tumbling/sliding windowing
Construct unified analytics and machine learning storage architectures on the cloud.
- ✓ AWS Data Stack: S3 data lake architecture, AWS Glue Crawlers, Glue Data Catalog, and Amazon Athena
- ✓ Databricks Lakehouse: Medallion Architecture (Bronze, Silver, Gold layers) and Unity Catalog governance
- ✓ Data Ingestion: Auto Loader for incremental file loading into cloud object storage
- ✓ Compute Optimization: Serverless Databricks clusters and spot instance workload scheduling
Ensure end-to-end data pipeline integrity, SLA adherence, and observability.
- ✓ Data Quality Frameworks: Great Expectations assertions and Soda Core automated validation
- ✓ Data Observability: Tracking schema changes, pipeline freshness, volume anomalies, and lineage with Monte Carlo
- ✓ Disaster Recovery & Data Replication: Cross-region replication, snapshotting, and point-in-time recovery
- ✓ Production Capstone: Building a complete end-to-end streaming and batch analytical platform
Tools & Technologies
Hands-on enterprise stack used across the program.
6-Month Career Transition Roadmap
A structured, milestone-driven progression designed to take you from foundational concepts to production-grade deployment and enterprise employment.
Foundations & Core Engineering
Master fundamental syntax, systems architecture, statistical principles, and core tooling required to build reliable technical foundations.
Advanced Implementation & Labs
Deep-dive into production frameworks, cloud integrations, automated pipelines, and enterprise-grade hands-on laboratory architectures.
Guaranteed Corporate Internship
Work on real corporate projects during your guaranteed 6-month paid internship* with monthly performance stipends and verified industry credentials.
Placement & Career Advancement
Access 1-on-1 interview preparation, resume branding, mock evaluations, and direct recruitment pathways across our 500+ corporate partners.
Production Capstone Projects
Build a portfolio of enterprise-grade projects evaluated by working tech leaders.
Multi-Cloud Enterprise Lakehouse Architecture
Delta Lake and Snowflake lakehouse ingesting batch and CDC telemetry from 5 distinct ERP and CRM sources.
High-Throughput Financial Ticker Stream Ingestion
Real-time streaming ingestion pipeline handling 50,000 events/sec with Apache Kafka and Spark Structured Streaming.
Production Data Pipeline Orchestrator with Airflow
Automated DAG workflow managing dependency reconciliation, SLA alert escalation, and data quality testing via Great Expectations.
Automated Data Observability & Anomaly Detector
Metadata tracking pipeline scanning ingestion anomalies, column distribution drift, and missing schema alerts.
Top 50 SQL Interview Questions for Data Pipelines & Analytics
Essential SQL query patterns, complex aggregations, windowing, and performance tuning asked in data engineering interviews.
Career Opportunities & Outcomes
Prepare for top-tier roles across our 500+ corporate recruitment partners.
High-Demand Career Tracks:
Frequently Asked Questions
Everything you need to know about admissions, curriculum, and internships.
This program is beginner-friendly and starts from fundamental core principles before advancing to production-grade enterprise implementations. Prior exposure to basic computing or programming is helpful but not mandatory.
Upon fulfilling course benchmarks and capstone assessments, you are placed into a guaranteed 6-month corporate paid internship* offering competitive monthly performance stipends with verified corporate experience letters and industry credentials.
You will earn official credentials and digital badges aligned with IBM Big Data Aligned, alongside an official AI Campus Certificate of Mastery that is verifiable on LinkedIn and recognized by 500+ corporate hiring partners.
The program fee of ₹80,000 (inclusive of GST) can be paid via 0% interest no-cost EMI starting from ₹6,666/month with zero down payment required.
Graduates qualify for high-growth roles in AI & Data with corporate salary packages typically spanning from ₹8 - 26 LPA, backed by our dedicated 100% placement support team.
Confused about your career path? Don't wait — talk directly to a mentor.
Get transparent, personalized guidance on curriculum, eligibility, scholarship waivers, and the guaranteed monthly paid corporate internship*. No sales pressure — just honest advice from working tech leaders.
