Position Overview: We're hiring a senior engineer to build the data foundation and the governed AI services that run on top of it. You will own pipelines from source to model in Python and dbt, ship them on Docker and Kubernetes in AWS, and expose trusted data to agents through Model Context Protocol (MCP) servers. This is a hands-on role with broad reach.
You will move between traditional data engineering and applied AI work, and partner directly with business teams to turn requests into working solutions that people rely on.
Data engineering Design, build, and operate batch and incremental pipelines in Python, with dbt for transformation, testing, and documentation, and fix issues at the source. Containerize workloads with Docker and run them on Kubernetes, using infrastructure as code and CI/CD to ship safely and often. Build on AWS services for storage, compute, orchestration, and security, and tune for cost and performance.
AI and agentic engineering Design and build MCP servers and connectors that give agents governed access to enterprise data and tools. Deploy and operate agentic workflows in production, including tool use, orchestration, evaluation, observability, and guardrails. Set patterns for authentication, access control, and auditability, and test new AI techniques so only the ones that matter reach production.
Team and business impact Work with stakeholders to define the problem, agree on the outcome, and deliver in small, visible steps. Review