About the Role:

We're looking for a Senior Data Engineer with solid 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.

Location: Bangalore

Experience: 6+ Years

What You'll Do:

- 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.
- Document data models and pipeline logic for maintainability, keeping compliance/audit

needs in mind.

What We're Looking For:

Must-Have:

- 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.

Strongly Preferred (Uber Internal Tooling): Piper/uWorc, Databook, uSecret, DSW, SourceGraph, Query Builder, OneETL.

Nice-to-Have:

Hive/Hadoop or Spark experience

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