Description
We're looking for a data engineer to build that platform across two tightly connected fronts.
First, you'll expand the Battery Data Warehouse (BDW) — a mature, exceptionally clean dataset that spans the entire battery product development lifecycle: raw materials and characterization, fabrication, performance testing, simulation and modeling, qualification, manufacturing, and field telemetry. You'll build reliable pipelines that bring this data — structured, semi-structured, and unstructured — out of disparate systems owned by teams around the world. A big part of the job is technical; an equally big part is human: earning the trust of source-system owners, opening up new integration opportunities, and establishing and enforcing the SLAs that keep BDW dependable.
Second, you'll build out BARD, the natural language interface to BDW. Done well, BARD will fundamentally change how battery engineers interact with their data — not just replacing dashboards and SQL with conversation, but pairing it with on-demand, in-line charting for real-time analysis and new ways to explore data. Think of it as giving every engineer their own personal data scientist. You'll engineer the full agentic stack: our custom MCP server, agentic search, domain knowledge, tool design, evals, and the end-to-end user experience.
The role combines data engineering and AI engineering work and calls for someone who’s both highly self-directed and an exceptional collaborator. You'll take real ownership and drive projects forward, while staying closely aligned with the team and our broader direction.
BS in Computer Science, Engineering, or a related field
Experience with Python, SQL, and at least one other high-level programming language
Experience building production data pipelines (ETL/ELT)
MS in Computer Science, Engineering, or a related field with 3+ years of relevant industry experience
Software engineering background and strong database fundamentals: data modeling, schema design, indexing, normalization, ACID, and OLTP vs. OLAP
Hands-on database development (DML, DDL, materialized views, stored procedures); Snowflake (streams, tasks, dynamic tables) a plus
Hands-on experience with orchestration (e.g., Airflow), batch/stream processing, and cloud platforms (e.g., AWS)
Deep curiosity about AI and hands-on experience applying it; you keep up with the latest tools, use AI daily (including for coding), and have strong intuition for tokenization, embeddings, context engineering, eval frameworks, and MCP servers, as well as a clear sense of where AI excels and where it doesn't (e.g., generating new code vs. maintaining complex existing code)
Experience securing AI/LLM systems that process sensitive or regulated data, including prompt injection defense, data handling policies, and audit trail requirements
Excellent written and verbal communication skills with both technical and non-technical audiences
Familiarity with batteries or other deep-tech / hardware engineering domains
