Senior Full Stack Engineer (Data, AI)
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Summary
The Senior Full Stack Engineer will design and maintain end-to-end data pipelines, storage architectures, and infrastructure for data quality and governance. The role involves building extensible systems to support AI and retrieval-based features while collaborating on platform and deployment decisions.
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