What if the way an entire engineering organization worked with its data could be reinvented? On Apple"s Battery Engineering team, you"ll build the data systems and AI interface that battery engineers across the company rely on — reliable pipelines feeding one of the cleanest and largest battery datasets anywhere, and a natural language interface that"s changing how engineers work with that data. You"ll be building the platform the whole battery organization runs on. It"s a rare chance to sharpen your data engineering craft and immerse yourself in applied AI at once — on a mission where your work shows up in the products millions of people use every day.
We"re looking for a data engineer to build that platform across two tightly connected fronts.\\n\\nFirst, 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.\\n\\nSecond, 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.\\n\\nThe 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.
Partner with cross-functional and engineering teams to identify data opportunities, define domain ontology, and establish the use cases that drive BDW\\nDesign, build, and maintain production data pipelines (ETL/ELT) that bring structured, semi-structured, and unstructured data into BDW at the right cadence and reliability\\nBuild relationships with upstream source-system owners to unlock new data integrations, and establish and enforce pipeline SLAs\\nEngineer BARD, the natural language interface to BDW — designing the agentic stack (MCP server, agentic search, domain knowledge, tool design, evals) and its end-to-end user experience\\nPartner with infrastructure teams (DBA, IT) to ensure the health of pipelines and the data warehouse\\nApply AI to your own workflow and to the battery organization"s problems — bringing strong intuition for context engineering, embeddings, tokenization, and evals\\nDeliver data analyses that drive critical decisions in battery research, development, and qualification
BS in Computer Science, Engineering, or a related field\\nExperience with Python, SQL, and at least one other high-level programming language\\nExperience building production data pipelines (ETL/ELT)
MS in Computer Science, Engineering, or a related field with 3+ years of relevant industry experience\\nSoftware engineering background and strong database fundamentals: data modeling, schema design, indexing, normalization, ACID, and OLTP vs. OLAP\\nHands-on database development (DML, DDL, materialized views, stored procedures); Snowflake (streams, tasks, dynamic tables) a plus\\nHands-on experience with orchestration (e.g., Airflow), batch/stream processing, and cloud platforms (e.g., AWS)\\nDeep 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)\\nExperience securing AI/LLM systems that process sensitive or regulated data, including prompt injection defense, data handling policies, and audit trail requirements\\nExcellent written and verbal communication skills with both technical and non-technical audiences\\nFamiliarity with batteries or other deep-tech / hardware engineering domains
