Senior Data Engineer

Summary

At Apple, we develop revolutionary technologies for the products that will define how we communicate in the future. The Zurich Vision Lab is an R&D team based in Zürich; we have shipped features like Persona, Animoji, Portrait Mode, and FaceTime Eye Contact, doing cutting-edge research while consistently shipping products. We collect and work with large datasets, and we build the infrastructure behind them. We are looking for a hands-on senior data engineer to own the data-management foundation for our machine-learning and feature-development work: the storage, pipelines, and quality controls that serve our internal customers so we can build amazing new products together.

Description

You will solve real, Apple-scale challenges, leading development of the internal-facing data infrastructure that enables the next generation of machine-learning and computer-vision projects: running data pipelines at scale in cloud environments. This is a hands-on, end-to-end role at the intersection of data engineering, DevOps, and machine learning, in that order.

Responsibilities

• Design and operate the managed storage for large-scale datasets and metadata, with versioning that lets consumers pin, reproduce, and roll back with confidence.
• Build and automate the ingestion, transformation, and publishing pipelines that move data through its full lifecycle reliably and at scale, and monitor them in production.
• Establish managed data quality: validation, lineage, and clear governance. So teams can trust the data they build on.
• Provide the tooling and interfaces that make datasets easy to discover, assemble, and consume across our machine-learning and feature-development processes.
• Support data-collection and synthetic-data-generation pipelines that bring new data into the system and scale our training data.
• Partner directly with the researchers and engineers who depend on this data, with a service mindset, automating toil rather than accepting it.

Minimum Qualifications

• Experience with distributed system design and automation, and strong software engineering fundamentals.
• A track record of architecting, implementing, and operating production data pipelines end to end.
• Strong SQL across engines such as Postgres, Trino, or SparkSQL, and working knowledge of columnar and lakehouse storage formats such as Parquet, Iceberg, or Delta.
• A demonstrated bias toward improving the process: automating toil and building tooling rather than settling for the status quo.
• Great interpersonal skills, a self-driven and customer-oriented attitude, and strong communication skills in English.

Preferred Qualifications

• Experience running data pipelines and distributed compute at scale with tools such as Dagster, Airflow, Ray, Prefect, Temporal, DBOS etc.
• Proficiency with cloud deployments: AWS, GCP, Kubernetes, Pulumi, etc.
• Exposure to MLOps: developing, deploying, and monitoring ML systems, with dataset and model versioning.
• Familiarity with dataframe engines such as Pandas, Polars, Daft, or Spark.
• Experience building tools, platforms, or SDKs that other engineers rely on; computer vision or computer graphics experience is a plus.