6 months ago
Bellevue, WA, USAStaff+
Responsibilities
- Design and implement reusable agentic AI frameworks for schema discovery, semantic interpretation, ETL transformation generation, SQL execution, and end-to-end data troubleshooting.
- Own data engineering architecture and technical direction for medallion-style lakehouse pipelines, layer boundaries, schema evolution, and data quality standards.
- Partner with product, science, data-tools platform, customers, and internal stakeholders to align data models, semantics, schema contracts, and business validation rules.
- Define and implement data testing, quality checks, observability, SLOs, monitoring, incident response, backfill and replay strategies, and reliability improvements.
- Own the interface between data and ML pipelines by defining schema-bound datasets and reliable writes to the semantic layer.
- Provide hands-on technical leadership, mentor team members, and establish AI-native data engineering practices.
Requirements
- Degree in Computer Science or another data-intensive field, with principal-level experience.
- 10+ years of professional development experience, including 8+ years of hands-on SQL and Python experience.
- Strong familiarity with at least one large-scale data processing engine such as Spark.
- 8+ years of experience across data management, modern warehouses or lakehouses, ETL and validation, and schema design in complex domains.
- Experience owning large-scale production data systems in distributed environments, including on-call support, incident handling, and lasting reliability improvements.
- Experience defining and adopting standards for data quality, observability, anomaly detection, reliability, testing, and data contracts.
- Ability to provide technical leadership through ambiguity, mentor others, and communicate with customers and technical and non-technical partners.
- Prior experience in supply chain, planning, or fulfillment domains is a plus.
Tech Stack
Categories
Data Engineering
