We're looking for a Tech Lead to own a delivery portfolio within a Manufacturing Engineering group that builds the data products a production plant runs on. This is a hands-on leadership role: you'll lead a small, senior, multi-disciplinary team shipping and operating manufacturing data products — real-time batch progression monitoring, process anomaly detection, and yield/throughput analytics — in a validated production environment. You'll apply AI with sound, regulated-context judgment, and you'll carry direct accountability for what ships, when it ships, and how it performs once it's live.
What You'll Do
- Own delivery of a portfolio of manufacturing data products from concept through validated production use.
- Lead a small, senior, multi-disciplinary team of data engineers, warehouse engineers, and full-stack developers.
- Unblock the team on hard technical problems — pipeline failures, warehouse performance and cost, data quality, plant systems integration, and production incidents.
- Supervise progression from DEV to QA to PROD, ensuring validation, change control, traceability, and documentation are properly met.
- Partner with data science to frame solvable problems, provide well-understood data and environments, and assess whether models are fit for intended production use.
- Evaluate and introduce AI-assisted approaches to development, testing, documentation, and monitoring — with clear judgment on where regulated-context limitations apply.
- Work closely with Product Owners and Product Architects to shape scope and sequencing, and communicate progress, risk, and trade-offs clearly.
- Produce architecture documentation, decision records, and operational runbooks that support long-term delivery and operations.
What You Bring
- Degree in engineering, computer science, or a related technical discipline, or equivalent demonstrated capability.
- Three or more years leading technical delivery for a software or data team.
- Advanced SQL with strong data modeling judgment and performance/cost awareness.
- Strong Python for production data engineering and services.
- Hands-on AWS experience across compute, storage, orchestration, identity, and observability.
- Deep experience with a cloud data warehouse; Snowflake preferred.
- Fluency with Git-based workflows, CI/CD, infrastructure as code, and structured environment promotion and release management.
- Working understanding of the machine learning lifecycle, including model evaluation, deployment, and monitoring.
- Strong stakeholder communication skills, with the judgment to challenge requests with better technical paths.
- Good understanding of agentic AI.
Nice to Have
- Prior experience in a regulated or validated manufacturing environment (e.g., pharma, medical device, semiconductor) with familiarity with concepts like GxP or 21 CFR Part 11.
- Experience with orchestration tools such as Airflow or dbt.
- Containerization/IaC depth (Docker, Kubernetes, Terraform).
- AWS or SnowPro certification.
- Experience building or evaluating agentic AI workflows in a production setting.
