Book a free demo class
Flagship career programme · Intermediate–Advanced

Data Engineering & Analytics Engineer Career Program

Design modern data pipelines, lakehouses, warehouses and analytics systems for enterprise-scale decision making.

Request demo detailsDownload the detailed syllabus
Talk to an adviser on WhatsApp
School of Data Engineering & Analytics learning pathway
Analytics FoundationsBusiness IntelligenceModern Data EngineeringCloud Data Architecture
ExcelGoogle SheetsSQLPythonPostgreSQL
Duration8–10 months
Batch startsConfirm with admissions
Learning formatLive mentor-led learningConfirm cohort format with admissions
Curriculum8 phasesIncluding a flagship capstone
Portfolio4 portfolio examples
LevelIntermediate–AdvancedProgramme level
PathwayData Engineer / Analytics Engineer / BI EngineerRelated career pathway

How you will learn

Live classes
Labs
Projects
Capstone
Mentor feedback
Career preparation
Programme curriculum

What you will learn, phase by phase

Explore the eight-phase pathway and its related syllabuses. Some phases use selected topics or alternative course options.

01Phase 1Analytics FoundationsBuild SQL, Python, statistics and analytics fundamentals.
Topics you will cover
  • Analytics Foundations and Business Questions
  • SQL Foundations for Analytics
  • Advanced SQL and Analytical Queries
  • Python Data Analysis with Pandas
  • Statistics for Data Analysts
  • Exploratory Data Analysis and Visualization
  • Analytical Data Products and Automation
  • Data Analytics Capstone
02Phase 2Business IntelligenceBuild governed semantic models and Power BI dashboards.
Topics you will cover
  • Power BI Workflow and Data Connectivity
  • Power Query and Data Preparation
  • Data Modeling and Star Schema
  • DAX Measures and Calculation Context
  • Visual Analytics and Data Storytelling
  • Security, Governance and Self-Service BI
  • Performance and Advanced Analytics
  • Power BI Capstone
03Phase 3Modern Data EngineeringEngineer batch/streaming lakehouse and warehouse pipelines.
04Phase 4Cloud Data ArchitectureApply data workloads on a selected major cloud platform.
05Phase 5DataOps + ReliabilityAdd CI/CD, IaC, observability and reliable operations to data platforms.
06Phase 6Governance + Data QualityImplement lineage, ownership, quality gates and access controls.
07Phase 7Industry SprintSolve a real analytics/data-platform problem with stakeholder requirements.
08Phase 8Flagship CapstoneBuild a modern data platform that feeds analytics-ready and BI-ready products.
Applied portfolio

Portfolio examples and flagship capstone

Explore project examples and the integrated flagship capstone. Confirm your cohort’s project selection with admissions.

Portfolio example 1

Executive Revenue Analytics

Analyze revenue, customers and products using SQL/Python and present actionable insights.

SQL scripts · notebook · charts · executive summary.
Portfolio example 2

Executive Sales and Margin BI

Build a governed Power BI model with DAX, drill-through, RLS and executive dashboard.

PBIX · data model diagram · DAX measures · dashboard walkthrough.
Portfolio example 3

Lakehouse-to-Warehouse Data Platform

Ingest raw data, process with Spark/Databricks, curate in Snowflake and serve analytics.

Pipeline code · architecture · quality tests · lineage · performance report.
Portfolio example 4

Highly Available AWS Application Platform

Deploy a resilient web/API workload with VPC, compute, database, monitoring, security and IaC.

Architecture diagram · Terraform/CloudFormation · deployed workload · runbook.
Flagship capstone

Modern Data Platform & Executive Analytics Product

Learners integrate the complete pathway into a production-style capstone with a clear brief, architecture or process design, working implementation, testing or evaluation evidence, documentation, mentor review and a final technical or business presentation.

Project brief and architecture or process design. · Working implementation and testing or evaluation evidence. · Documentation and final technical or business presentation.
Programme value

Why this programme

Build the data foundations, pipelines, analytics layers and decision systems modern organisations rely on.

Programme fit

Who this programme is for

Students, analysts, SQL/Python users and technology professionals targeting data engineering, analytics engineering or BI engineering roles.

Intermediate–AdvancedData Engineer / Analytics Engineer / BI Engineer
PrerequisitesBasic spreadsheet familiarity is helpful. The pathway introduces SQL, Python and data concepts before progressing to engineering platforms.
Practical capabilities

What you will be able to do

  • Build SQL, Python, statistics and analytics fundamentals.
  • Build governed semantic models and Power BI dashboards.
  • Engineer batch/streaming lakehouse and warehouse pipelines.
  • Apply data workloads on a selected major cloud platform.
  • Add CI/CD, IaC, observability and reliable operations to data platforms.
  • Implement lineage, ownership, quality gates and access controls.
  • Solve a real analytics/data-platform problem with stakeholder requirements.
  • Build a modern data platform that feeds analytics-ready and BI-ready products.
Tools and platforms

Technology you will use in this programme

ExcelGoogle SheetsSQLPythonPostgreSQLMySQLSQLiteCloud warehouse SQLpandasNumPyJupyterSciPystatsmodelsMatplotlibPlotlyGitSchedulerGitHub
Career relevance

Data Engineer / Analytics Engineer / BI Engineer

Prepare for Data Engineer, Analytics Engineer and BI Engineer roles through practical skills, project evidence and interview preparation. Employment is not guaranteed.

Programme evidence and instruction

Programme guidance

Discuss your learning pathway

Review prerequisites, learning format and project expectations with admissions before enrolment.

Get programme guidance
Project evidence

Explore the programme projects

Review the project briefs and deliverables to understand the work expected during the programme.

Review project expectations
Programme FAQs

Clear answers before you enrol

How is a flagship programme different from a focused course?

A focused course develops one defined capability. A flagship programme combines foundations, specialist skills, production practices, capstone work and career preparation into a longer role-based journey.

How much time should I plan each week?

Confirm live class times and the expected weekly commitment with admissions before enrolment.

What practical work will I complete?

The curriculum includes guided labs, project work and a flagship capstone. Review the portfolio examples above and confirm cohort-specific project requirements with admissions.

How will my progress be assessed?

Progress is reviewed through guided labs, project work, implementation and evaluation evidence, and a final capstone presentation.

Are vendor certifications included?

The curriculum develops practical skills. Vendor certification exams and official certification status are not included unless explicitly confirmed in your enrolment offer.

Does this programme guarantee a job?

No. Employment depends on learner performance, experience, hiring conditions and employer decisions.

Will I receive a certificate?

A programme completion certificate may be issued after you meet the published attendance, assignment, project and assessment requirements.

Ready to start?

Ready to explore the Data Engineering & Analytics Engineer pathway?

Review the curriculum and confirm prerequisites, fees and the next available cohort with admissions before enrolment.

Request demo details Speak with an adviser: +91 73030 11395
A-56, Sector-64, Noida, Uttar Pradesh – 201301