Data Engineer

    Design, develop, and maintain scalable ETL and ELT pipelines for batch and scheduled data processing.

·    Build and optimize data transformations using SQL, Python, and PySpark.

·    Ingest and integrate data from databases, APIs, files, SharePoint, and cloud storage.

·    Create and maintain curated, analytics-ready datasets for Power BI, Superset, and other reporting platforms.

·    Implement data-quality checks, reconciliation controls, monitoring, logging, and failure alerts.

·    Troubleshoot pipeline failures, schema mismatches, performance bottlenecks, and data discrepancies.

·    Optimize large-scale data processing for runtime, memory, storage, and cost efficiency.

·    Automate recurring workflows and schedule production jobs using orchestration tools such as Apache Airflow.

·    Maintain technical documentation, data mappings, business rules, and operational runbooks.

·    Collaborate with BI analysts, application teams, and business stakeholders to deliver dependable data solutions.