Senior Data Engineer

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.

• Own data architecture and system design decisions for pipelines and data models - grain, partitioning, schema evolution, and scalability tradeoffs.

Take end-to-end ownership of pipelines in production

design, build, deploy, monitor, and operate.

• Design and build transaction tables (append-only, immutable event/transaction-grain fact tables) alongside standard fact/dimension models using medallion (bronze/silver/gold) architecture.
• Build and support pipelines for finance, risk, or compliance use cases where accuracy, auditability, and data lineage are critical.

Implement data quality and auditability controls

validation checks, reconciliation logic, anomaly detection, and audit trails for every pipeline you own.

Write complex, cross-dialect SQL (MySQL + PostgreSQL)

window functions, multi-layered CTEs, CASE-driven logic, COALESCE/NULLIF, type casting, and date/timestamp handling.

• Build idempotent reload patterns (DELETE+INSERT), UNION ALL/set operations, and templated (Jinja-style) SQL including handling for late-arriving/corrected transaction records without double-counting.
• Orchestrate Airflow-style DAGs (Pipeline/BaseTask, ExternalTaskSensor for cross-pipeline deps) with secure credential handling via uSecret.

Build Python ETL tooling

pandas (CSV ↔ DB), SQLAlchemy + raw drivers (MySQLdb, psycopg2), type hints/dataclasses, class-based pipeline design.

• Integrate with Google Drive/Sheets APIs; build YAML-driven pipeline configs; handle Piper staging/user_staging quirks.
• Own CI/CD for pipelines via standard Git workflows.

Use Uber's internal stack day-to-day

OneETL for pipeline authoring, Piper/uWorc for scheduling, Databook for lineage, uSecret for credentials, DSW notebooks for CSV/table work, SourceGraph for code search, Query Builder for ad hoc SQL.

• 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.