Overview
Responsibilities:
- Pipeline Architecture: Build and maintain scalable Python ETL pipelines for core financial datasets (Actuals, Budgets, Headcount, Allocations)
- Data Integration: Design strategies to extract data from various APIs and databases, implementing incremental loads and efficient warehousing logic
- Transformation & Modeling: utilize Numpy, Pandas or Polars to transform raw logs into clean, documented, and modeling-ready datasets for Power BI
- Data Quality Assurance: Engineer automated quality checks (reconciliation logic, mapping validation, anomaly detection) to ensure trust in the numbers
- Operational Excellence: Own the deployment routines, including scheduling, logging, error handling, and retry mechanisms
Required Qualifications:
- Advanced skills in Python for data processing, ability to write clean, modular, and reusable code
- Experience with Pandas is required; familiarity with Polars is a plus
- 3+ years of experience building automated reporting systems or data products
- Strong SQL skills ( joins, window functions, CTEs) and understanding of performance optimization
- Experience in working with APIs, handling pagination and authentication
- Understanding of financial reporting concepts (like reconciliation, general ledgers, and dimensional modeling)
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