ETL pipelines, Apache Spark, Databricks, Kafka and SQL. Specialised, well-paid, high-demand track.
By the end of the programme, you will ship real deliverables.
Deep SQL, query optimisation, and window functions.
pandas, ETL scripting, and scheduling with Airflow.
PySpark fundamentals through Databricks and cluster operations.
Stream processing, topics, partitions, and producer/consumer patterns.
Snowflake or BigQuery patterns and dimensional modelling.
A portfolio pipeline you built from ingestion to warehouse.
Each phase builds on the last. Live instruction with reviewable deliverables.
Advanced SQL, window functions, query plans, and dimensional modelling.
pandas, ETL scripting, file formats, and Airflow scheduling.
PySpark, transformations, joins at scale, and Databricks workflows.
Kafka architecture, producers, consumers, and stream processing patterns.
Snowflake / BigQuery, cost-aware design, and a capstone pipeline project.
Data engineer interview preparation, system design, and portfolio finalisation.
Data analyst wanting to own pipelines and infrastructure, not just queries.
Backend developer moving into a specialised, higher-paid data track.
Graduate with SQL fundamentals targeting a data engineer role.
Python, machine learning, deep learning, TensorFlow and LLM fundamentals. Project-based.
Power BI, Tableau, SQL and Python for analysis. Turn data into decisions.
AWS, Azure and GCP preparation. Structured mock tests and concept deep-dives.
A free demo class with the instructor. If it is not a fit, you owe nothing.