Analytics Data Engineer, Siri AI Quality Engineering

trust and use data to help drive operational and business decisions.

You will ultimately be part of a collaborative team that's responsible for understanding how well our products work, advocating for critical changes, and representing how they'll be experienced by our customers. We deliver descriptive, diagnostic, prescriptive, and predictive analytics that show what's happening, explain why, recommend what to do, and empower decision making automation. We publish certified metrics and models used in executive-facing reports, raising product quality, accelerating releases, and reducing risk across all Apple platforms and locales. As a member of our passionate group in Siri, you will have the unique and rewarding opportunity to shape and improve our products to delight and inspire millions of Apple's customers every day.","responsibilities":"Build curated, accurate, and reliable data that powers Siri AI

Design and run our lakehouse so analysts and data scientists can move fast with confidence

Design and evolve the lakehouse (i.e. Apache Iceberg): schemas, partitioning, compaction, and schema evolution

Build batch and streaming pipelines with Python/Spark/PySpark; orchestration with Apache Airflow (or similar)

Ingest from diverse sources; manage metadata, lineage, and the data catalog

Enforce data quality (tests, SLAs, monitors) and reliability (idempotency, replay, backfills)

Optimize cost and performance across storage and compute

Partner with analysts to deliver semantic layers, certified metrics, and feature tables

Document, review, and automate scripts, runbooks, CI/CD, and infra-as-code

Preferred Qualifications

Spark Streaming, Kafka, Flink or similar streaming; near-real-time analytics

Data-quality frameworks and observability

Experience with feature stores and ML data pipelines

BS/MS in an engineering or a related field (or equivalent)

Minimum Qualifications

5+ years in data engineering; expert SQL and Python knowledge

Deep Spark experience (batch + structured streaming)

Hands-on experience with a modern data lake technology: Iceberg or equivalent of object storage

Proven pipeline orchestration (Airflow or equivalent) and Git-based delivery

Strong data modeling, governance, lineage, and reliability practices

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .