Data Engineer, Battery Data Platform & AI

Apple • Cupertino, CA

1d

About The Position

This role is on Apple's Battery Engineering team, focusing on building data systems and an AI interface for battery engineers across the company. The position involves creating reliable data pipelines for a large and clean battery dataset, and developing a natural language interface to change how engineers interact with data. The work directly impacts products used by millions. The role requires building a platform for the entire battery organization, combining data engineering and applied AI. The responsibilities include expanding the Battery Data Warehouse (BDW), which contains data spanning the entire battery product development lifecycle (raw materials, fabrication, testing, simulation, qualification, manufacturing, and field telemetry). This involves building pipelines to integrate structured, semi-structured, and unstructured data from various systems globally. A significant part of the job involves human interaction, such as building trust with source-system owners, identifying integration opportunities, and establishing Service Level Agreements (SLAs) for BDW dependability. Additionally, the role involves building BARD, a natural language interface to BDW, which aims to transform engineer-data interaction beyond dashboards and SQL, incorporating conversational capabilities, on-demand charting, and new data exploration methods. This is envisioned as providing each engineer with a personal data scientist. The engineering work covers the full agentic stack, including custom MCP servers, agentic search, domain knowledge, tool design, evaluations, and the end-to-end user experience. The role demands a self-directed and collaborative individual who can take ownership and drive projects while aligning with team and company direction.

Requirements

• 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)
• 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

Nice To Haves

• MS in Computer Science, Engineering, or a related field with 0+ years of relevant industry experience
• Familiarity with batteries or other deep-tech / hardware engineering domains

Responsibilities

• Partner with cross-functional and engineering teams to identify data opportunities, define domain ontology, and establish the use cases that drive BDW
• Design, build, and maintain production data pipelines (ETL/ELT) that bring structured, semi-structured, and unstructured data into BDW at the right cadence and reliability
• Build relationships with upstream source-system owners to unlock new data integrations, and establish and enforce pipeline SLAs
• Engineer 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
• Partner with infrastructure teams (DBA, IT) to ensure the health of pipelines and the data warehouse
• Apply AI to your own workflow and to the battery organization's problems — bringing strong intuition for context engineering, embeddings, tokenization, and evals
• Deliver data analyses that drive critical decisions in battery research, development, and qualification

What This Job Offers

• Job Type: Full-time
• Career Level: Senior