Data Engineer

Build and maintain Data Factory pipelines for API-based data ingestion (REST, OData).

Develop Python ETL scripts for custom extraction, API polling, and file ingestion.

Develop and maintain Spark/Python notebooks (Python/PySpark) and SQL-based transformation logic.

Create Dataflows Gen2 (Power Query) for handling Excel/CSV-based messy data.

Implement incremental loads, watermarking, and CDC patterns.

Integrate data from Dataverse and other enterprise sources into Fabric.

Own Bronze → Silver transformations (cleansing, deduplication, standardisation)

Support Silver → Gold transformations (star schema, fact/dimension modelling)

Prepare feature datasets for AI/ML workloads and support notebook-based model development

Implement data quality frameworks (validation rules, anomaly detection)

Build and optimise Power BI semantic models (Direct Lake)

Prepare datasets for reporting and basic analytics use cases.

Monitor pipeline performance, platform health, and Fabric capacity (F64+), including cost optimisation.

Maintain documentation (data dictionaries, pipeline inventory, architecture decisions)

• Manage Fabric capacity — pause/resume scheduling, scaling, and cost optimisation