M60 - Full Stack Engineer
Summary
Senior Full Stack Engineer with a focus on data infrastructure, designing and owning end-to-end data pipelines, storage, and retrieval systems while contributing to broader product architecture and AI-ready system design.
Overview
We're looking for a senior software engineer with strong data engineering experience to take ownership of the data infrastructure underpinning our products. This is a hands-on role with significant autonomy. You'll work closely with the product and engineering team, playing a leading role in technical decisions on how data pipelines, storage, and retrieval systems get built.
Beyond data engineering, you'll bring solid software engineering fundamentals to help design and build broader system architecture. As data engineering workload evolves, you'll work alongside our other software engineers to help deliver product features, so this role isn't confined to data engineering alone. You should be equally comfortable architecting a data pipeline and contributing to backend systems that power our products.
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
Requirements
- 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