Tech Lead - Manufacturing Data & AI Platforms

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.