Data Engineer, Battery Data Platform & AI

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.

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.

MINIMUM QUALIFICATIONS

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)

PREFERRED QUALIFICATIONS

MS in Computer Science, Engineering, or a related field with 0+ 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