Senior Full Stack Engineer (Data, AI)

Job Description

Key Responsibilities

. Own end-to-end design and delivery of data pipelines, from ingestion to transformation to serving

. Design data models and storage architectures that support both operational and analytical workloads

. Build and maintain infrastructure for data quality, observability, and governance

. Contribute to broader product and platform architecture, working alongside other software engineers as priorities shift

. Design systems that are extensible enough to support AI/retrieval-based features over time

. Contribute significantly to key technical decisions, escalating trade-offs where they intersect with broader priorities

. Collaborate with stakeholders on platform and deployment decisions

. Work with attention to data sensitivity and system constraints in a regulated environment

Qualifications

Technical Requirements

Required

. 5-7+ years of professional software engineering experience, with demonstrated ownership of production data systems end-to-end

. Strong data engineering fundamentals: ETL/ELT pipeline design, data modeling, batch and streaming processing

. Strong proficiency in at least one general-purpose programming language, with a track record of building production-grade backend systems, not just data scripts or pipelines

. Solid software engineering fundamentals: API design, system architecture, ability to work across the stack when needed

. Experience working with cloud-native data platforms or lakehouse architectures

. Comfortable operating with significant autonomy and taking a leading role in technical decisions

. Strong communication skills able to explain technical trade-offs to non-technical stakeholders

Good to have:

. Experience with Databricks, Unity Catalog, Delta Lake, or similar lakehouse tooling

. Experience building data pipelines to support retrieval-augmented generation (RAG) or other AI/ML workflows, e.g. embedding generation, vector store population

. Experience in government, public sector, or other regulated environments with data sensitivity requirements

. Experience with cloud-native deployment platforms