About this role
We're looking for a Senior Data Engineer with strong data architecture and system design skills to design, build, and own end-to-end production pipelines powering analytics and decision-making across the org. The role centers on deep Python + SQL expertise, robust transaction-level fact tables, and rigorous data quality and auditability, ideally within a finance, risk, or compliance data domain. Internal candidates familiar with Uber's data platform stack (Piper/uWorc, Databook, uSecret, DSW, SourceGraph, Query Builder, OneETL) will be prioritized.
● 6+ years in data engineering. ● Strong data architecture and system design skills to make sound tradeoffs on modeling, partitioning, and scalability, not just write queries. ● Demonstrated end-to-end production ownership of data pipelines from design through deployment, monitoring, and operational support. ● Expert-level Python + SQL as the primary toolset. Hive/Hadoop is a nice-to-have only. ● Robust data quality and auditability practices - validation, reconciliation, anomaly detection, audit trails. ● Experience working with finance, risk, or compliance data - comfort with the heightened accuracy, auditability, and data-lineage requirements typical of these domains. ● Proven experience designing and building transaction/fact tables. ● Expert SQL (window functions, CTEs, CASE logic, cross-dialect MySQL/PostgreSQL). ● Strong Python (pandas, SQLAlchemy, type hints/dataclasses, class-based pipelines). ● Airflow-like orchestration experience (Piper/uWorc a plus). ● Solid data modeling fundamentals (fact/dim, normalization, medallion architecture). ● YAML-based config experience; standard CI/CD and Git workflows.