Duration4–5 months9–11 hours per week
All Levels · SQL · Spark · Cloud
Data Engineer Program
Design, build and maintain scalable data pipelines, cloud data platforms, data lakes, warehouses, ETL/ELT workflows, batch processing and real-time analytics infrastructure.
Follow a structured 4–5 months roadmap from foundations to applied data engineer capability. Practise through 12+ projects, guided labs, case studies and mentor-reviewed assignments. Use Python, SQL, Apache Spark, Kafka in realistic workflows rather than disconnected demonstrations.
A-56, Sector-64, Noida, Uttar Pradesh – 201301 Transparent program guidance
PythonSQLApache Spark
Flagship pathway12+ projects and a portfolio-ready capstone
Program at a glance
Next batch23 Aug 2026Open for Registration
Learning modeLive Online / ClassroomSQL · Spark · Cloud
LevelAll LevelsFoundations and guided progression
Projects12+ projectsGuided work plus capstone
CertificateCompletion certificatesubject to published attendance, assignment, project and assessment criteria.
Designed for applied outcomes
Why learners choose this program.
01
Follow a structured 4–5 months roadmap from foundations to applied data engineer capability.
02
Practise through 12+ projects, guided labs, case studies and mentor-reviewed assignments.
03
Use Python, SQL, Apache Spark, Kafka in realistic workflows rather than disconnected demonstrations.
04
Build a portfolio-ready capstone and explain the decisions, validation and results.
05
Prepare for Data Engineer and Cloud Data Engineer role conversations with structured career guidance.
Flagship program curriculum
Review every module and the skills covered.
Open each module to compare the detailed syllabus with your current skills and target role before enrolment. Curriculum reviewed 08 Jul 2026.01Module 1SQL and Database Foundations
What learners master
Relational databases, joins, subqueries, window functions, indexing, normalization, transactions, optimization and reporting queries.
Detailed syllabus
8 focused topicsTopics covered
- 01
Relational databases
- 02
joins
- 03
subqueries
- 04
window functions
- 05
indexing
- 06
normalization
- 07
transactions
- 08
optimization and reporting queries
02Module 2Python for Data Engineering
What learners master
Python scripting, file processing, APIs, JSON, CSV, Pandas, database connectivity, automation, error handling and validation.
Detailed syllabus
9 focused topicsTopics covered
- 01
Python scripting
- 02
file processing
- 03
APIs
- 04
JSON
- 05
CSV
- 06
Pandas
- 07
database connectivity
- 08
automation
- 09
error handling and validation
03Module 3Data Warehousing Concepts
What learners master
OLTP vs OLAP, star and snowflake schemas, fact and dimension tables, slowly changing dimensions, data marts and BI architecture.
Detailed syllabus
5 focused topicsTopics covered
- 01
OLTP vs OLAP
- 02
star and snowflake schemas
- 03
fact and dimension tables
- 04
slowly changing dimensions
- 05
data marts and BI architecture
04Module 4ETL and ELT Pipelines
What learners master
Extraction, transformation, loading, scheduling, orchestration, quality checks, incremental loading, CDC and workflow automation.
Detailed syllabus
8 focused topicsTopics covered
- 01
Extraction
- 02
transformation
- 03
loading
- 04
scheduling
- 05
orchestration
- 06
quality checks
- 07
incremental loading
- 08
CDC and workflow automation
05Module 5Apache Spark and Big Data
What learners master
Distributed computing, PySpark, DataFrames, Spark SQL, partitioning, tuning, batch processing and Parquet/Avro formats.
Detailed syllabus
7 focused topicsTopics covered
- 01
Distributed computing
- 02
PySpark
- 03
DataFrames
- 04
Spark SQL
- 05
partitioning
- 06
tuning
- 07
batch processing and Parquet/Avro formats
06Module 6Cloud Data Platforms
What learners master
AWS S3, Glue, Redshift, Azure Data Factory, Data Lake, Synapse, Google BigQuery, Cloud Storage and cloud-native pipelines.
Detailed syllabus
8 focused topicsTopics covered
- 01
AWS S3
- 02
Glue
- 03
Redshift
- 04
Azure Data Factory
- 05
Data Lake
- 06
Synapse
- 07
Google BigQuery
- 08
Cloud Storage and cloud-native pipelines
07Module 7Modern Data Stack
What learners master
Snowflake, Databricks, dbt, Airflow, lineage, catalogs, ELT patterns, analytics engineering and metric-layer concepts.
Detailed syllabus
8 focused topicsTopics covered
- 01
Snowflake
- 02
Databricks
- 03
dbt
- 04
Airflow
- 05
lineage
- 06
catalogs
- 07
ELT patterns
- 08
analytics engineering and metric-layer concepts
08Module 8Streaming Data Engineering
What learners master
Kafka, event-driven architecture, streaming pipelines, real-time ingestion, queues, Spark Streaming and monitoring.
Detailed syllabus
6 focused topicsTopics covered
- 01
Kafka
- 02
event-driven architecture
- 03
streaming pipelines
- 04
real-time ingestion
- 05
queues
- 06
Spark Streaming and monitoring
09Module 9Data Governance and Security
What learners master
Access control, privacy, encryption, masking, PII handling, audit logs, quality frameworks, metadata and compliance-oriented design.
Detailed syllabus
8 focused topicsTopics covered
- 01
Access control
- 02
privacy
- 03
encryption
- 04
masking
- 05
PII handling
- 06
audit logs
- 07
quality frameworks
- 08
metadata and compliance-oriented design
Tools and platforms
Use the practical stack behind the program.
Platform access and software requirements are confirmed before the cohort begins.PythonSQLApache SparkKafkaSnowflakeDatabricksAirflowdbt
Portfolio-ready capstone
Build work you can explain, validate and present.
Deliver a retail analytics warehouse, real-time sales pipeline, customer 360 platform, financial transaction data lake, healthcare reporting warehouse or streaming dashboard.Final applied project
Data Engineer capstone
- Project direction
- Deliver a retail analytics warehouse, real-time sales pipeline, customer 360 platform, financial transaction data lake, healthcare reporting warehouse or streaming dashboard.
- Evidence 01
- A documented problem statement, scope and success criteria.
- Evidence 02
- A working implementation with architecture or process documentation.
- Evidence 03
- Testing, validation and limitations recorded against a review checklist.
Portfolio evidenceA documented problem statement, scope and success criteria.
Portfolio evidenceA working implementation with architecture or process documentation.
Portfolio evidenceTesting, validation and limitations recorded against a review checklist.
Portfolio evidenceA portfolio case study and presentation-ready project walkthrough.
Who should enrol
Confirm that this program matches your goals and starting point.
Analysts, graduates, IT professionals, database learners, BI professionals and career switchers.- Live online classes, hands-on labs, mentor-led projects, case studies, assessments, a capstone project, interview preparation and portfolio building.
- Plan for 9–11 hours per week.
Learning methodology
Live guidance, deliberate practice and reviewable outputs.
Live expert instruction
Live online classes, hands-on labs, mentor-led projects, case studies, assessments, a capstone project, interview preparation and portfolio building.
Guided hands-on practice
Apply each major concept through labs, case studies and 12+ projects.
Milestone feedback
Progress is reviewed through sql and python practical checks, warehouse and pipeline assignments, platform design reviews, capstone reliability and architecture review.
Portfolio validation
Document implementation decisions, test results, limitations and business or technical value.
Assessment approachEvidence-based progress, not passive attendance
SQL and Python practical checks · Warehouse and pipeline assignments · Platform design reviews · Capstone reliability and architecture review
Career direction
Translate program work into a credible professional story.
Build role-aligned skills, project evidence and interview confidence for Data Engineer, Cloud Data Engineer, ETL Developer, Big Data Engineer, Analytics Engineer, Data Platform Associate, BI Data Specialist opportunities.- Data Engineer
- Cloud Data Engineer
- ETL Developer
- Big Data Engineer
- Analytics Engineer
- Data Platform Associate
- BI Data Specialist
Portfolio and project review
Improve project structure, documentation, evidence and the clarity of each walkthrough.
Resume and profile guidance
Connect verified program capabilities and project outputs to a focused professional profile.
Interview preparation
Practise explaining technical decisions, trade-offs, results and limitations with confidence.
Application planning
Identify relevant role families and create a practical, consistent application plan.
Career support is not an employment guarantee. Outcomes depend on experience, participation, project quality, interviews, employer requirements and market conditions.
Noida learning centre
Local guidance for learners across Delhi NCR.
FutureEdgeAI Academy supports live online and classroom-guided flagship program cohorts according to each published batch format.A-56, Sector-64, Noida, Uttar Pradesh – 201301Program FAQs
Clear answers before you enrol.
Who should join the Data Engineer Program?
Analysts, graduates, IT professionals, database learners, BI professionals and career switchers.
Do I need prior experience?
No prior data-engineering role is required. Basic spreadsheet or programming exposure is helpful, and SQL and Python foundations are included at the start of the pathway.
How much time should I plan each week?
Plan approximately 9–11 hours per week, including live sessions, guided labs, assignments, revision and portfolio work.
Are classes live or recorded?
The program includes live instructor-led classes. Recording access, notes, assignments and learning resources are provided according to the published cohort format.
What practical work will I complete?
12+ projects are included across guided labs, applied assignments and portfolio builds. The capstone direction is: Deliver a retail analytics warehouse, real-time sales pipeline, customer 360 platform, financial transaction data lake, healthcare reporting warehouse or streaming dashboard.
How will my progress be assessed?
SQL and Python practical checks; Warehouse and pipeline assignments; Platform design reviews; Capstone reliability and architecture review. Learners receive feedback at key milestones before the capstone review.
Which tools will I use?
Learners practise with Python, SQL, Apache Spark, Kafka, Snowflake, Databricks, Airflow, dbt. Published tools may be updated to reflect current workflows and platform availability.
Is career and placement assistance included?
Career support includes roadmap guidance, resume and profile improvement, portfolio reviews, mock interviews and opportunity visibility. Employment or placement is not guaranteed.
Will I receive a certificate?
A program completion certificate may be issued after the learner meets the published attendance, assignment, project and assessment requirements.
Current fee: ₹30,000. Payment options and refund terms are explained before payment. Review the academy's refund policy for published terms.
Learner proof
Practical learning, explained by the people who experienced it.
Published learner feedback from FutureEdgeAI pathways shows how guided projects, mentor review and career preparation can help learners communicate their work with greater confidence.5.0
“The SOC project helped me practise alert triage, investigation notes, incident response and the professional reporting expected from analysts.”
Arjun MehtaCybersecurity Learner
5.0
“Building one product end to end—React, APIs, authentication, databases, testing and deployment—made my portfolio much more credible.”
Priya MenonFull Stack Developer
5.0
“I progressed from AI fundamentals to building GenAI applications and a source-aware RAG assistant I could confidently demonstrate.”
Neha SinghApplied AI Learner
Individual learning and career outcomes vary by starting point, participation, project quality, experience and market conditions. Testimonials do not guarantee employment or placement.
Ready to explore Data Engineer Program?