Design, build, and operate scalable Extract, Transform, Load / Extract, Load, Transform (ETL/ELT) data pipelines (batch and real-time) to ingest, transform, and load data from multiple sources.
Engineer and maintain data platforms (data lakes and data warehouses) with strong reliability, availability, and operational resilience.
Implement system integrations using Application Programming Interfaces (APIs), streaming, and enterprise data integration tools to ensure consistent end-to-end data flows.
Apply Apache Spark / Python for Spark (Spark/PySpark) and advanced Structured Query Language (SQL) for large-scale processing, optimization, and perform data transformations.
Embed data quality, governance, security, and regulatory controls across pipelines and datasets.
Enable development and operations (DevOps) for data through Continuous Integration / Continuous Delivery (CI/CD), orchestration and scheduling, monitoring, troubleshooting, and production support.
Hands-on expertise in Prophecy pipeline development and deployment, PySpark analysis, and PySpark pipeline engineering (build and release), alongside strong core software engineering fundamentals
