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 centres 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.

Responsibilities:

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

Requirements:

• 6+ years in data engineering.
• Strong data architecture and system design skills to make sound trade-offs on modelling, partitioning, and scalability.
• 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.
• Robust data quality and auditability practices - validation, reconciliation, anomaly detection, audit trails.
• Experience working with finance, risk, or compliance data.
• Proven experience designing and building transaction/fact tables.
• Strong Python (pandas, SQLAlchemy, type hints/dataclasses, class-based pipelines).
• Airflow-like orchestration experience.
• Solid data modelling fundamentals (fact/dim, normalisation, medallion architecture).
• YAML-based config experience; standard CI/CD and Git workflows.