Senior Data Engineer at Apple

Zurich, Zurich, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

02 Sep, 26

Salary

0.0

Posted On

04 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Distributed System Design, Data Pipeline Architecture, SQL, Postgres, Trino, SparkSQL, Parquet, Iceberg, Delta Lake, DevOps, Machine Learning, Cloud Deployments, Kubernetes, MLOps, Python, Software Engineering

Industry

Computers and Electronics Manufacturing

Description

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. 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.

Responsibilities

Lead the development of internal data infrastructure and pipelines to support machine learning and computer vision projects. Own the data-management foundation, including storage, pipelines, and quality controls for internal customers.