About the role
Responsibilities:
- Engineering Leadership and People Management: Manage and mentor a team of senior data engineers, including hiring, performance reviews, regular 1:1s, and technical development.
- End-to-End Data Platform Ownership: Design, build, and scale production-grade data platforms from scratch, covering batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.
- Technical Leadership and Architecture: Lead system design discussions, review data schemas and architecture, conduct code reviews, and remain hands-on with Python and SQL.
- Data Quality and Reliability: Own data quality checks, pipeline SLAs, production incidents, and root-cause analysis to ensure reliable data operations.
- Data Ingestion and Integration: Oversee multi-source data ingestion, Change Data Capture (CDC), data lineage, data contracts, and cross-system data reconciliation.
- ML and Analytics Enablement: Build and support data pipelines for ML training and evaluation, dataset versioning, feature pipelines, and human-in-the-loop feedback workflows.
- Performance and Cost Optimization: Optimize cloud infrastructure and data processing costs while managing technical risks and communicating trade-offs to business stakeholders.
Requirements:
- Experience: 9+ years of overall software or data engineering experience, including 6+ years of hands-on data engineering experience and at least 2 years of direct people management of data engineers.
- Strong Technical Foundation: Proven hands-on experience writing and owning production code in Python and SQL, with the ability to review code, evaluate schemas, and critique architectural designs.
- Data Platform Ownership: Experience designing, building, and owning an end-to-end production data platform from the ground up, rather than simply maintaining an existing platform.
- Technical Architecture: Strong understanding of data platform architecture, including batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.
- Production Reliability: Experience owning data quality checks, pipeline SLAs, production incident management, and root-cause analysis (RCA).
- People Management: Proven experience directly managing, mentoring, and developing data engineers while remaining technically hands-on.