Why Data Engineering?
Every AI model, every dashboard, every business insight depends on clean, reliable data pipelines. Data engineers are the architects who build them. SkillCred's Data Engineering stream trains you across the modern data stack in 8 intense, project-driven weeks.
What You'll Build
Solo Project 1 — Multi-Source Data Extractor (Weeks 1–2)
Build a Python pipeline extracting data from 3+ sources (APIs, CSVs, databases), cleaning with Pandas, and loading into a PostgreSQL star schema with quality checks.
Solo Project 2 — Airflow-Orchestrated ETL Pipeline (Weeks 3–4)
Build an Airflow DAG orchestrating extract → Spark transform → validate (Great Expectations) → load, with retry logic and alerting.
Pair Project — Real-Time Streaming Dashboard (Weeks 5–6)
Build a Kafka → Spark Structured Streaming → dbt-managed warehouse → Grafana dashboard. One partner handles ingestion + streaming, the other builds warehouse + visualization.
Group Capstone Options (Weeks 7–8)
Choose from: Weather Analytics Platform, E-Commerce Data Warehouse, Social Sentiment Pipeline, Student Analytics Data Lake, or Log Analytics Engine.
8-Week Curriculum Overview
| Week | Phase | Key Topics |
|---|---|---|
| 1 | SQL Mastery & Modeling | CTEs, window functions, dimensional modeling, EXPLAIN |
| 2 | Python for DE | OOP, file formats (Parquet, Avro), APIs, Pandas, pytest |
| 3 | ETL/ELT & Orchestration | Airflow DAGs, Great Expectations, CDC, scheduling |
| 4 | Big Data with Spark | RDDs, DataFrames, SparkSQL, PySpark, optimization |
| 5 | Streaming & Real-Time | Kafka, Connect, Schema Registry, Structured Streaming |
| 6 | Warehousing & dbt | Kimball vs Inmon, dbt models, testing, BI integration |
| 7 | Cloud Data Platforms | AWS S3/Glue/Redshift, BigQuery, Delta Lake, governance |
| 8 | Capstone Pipeline & Demo | E2E assembly, monitoring, data lineage, documentation |
Career Outcomes
Graduates are prepared for Data Engineer, ETL Developer, Analytics Engineer, Data Platform Engineer, and Big Data Engineer roles.