Data Engineer: Health Sensor Data & Scalable Pipelines

Apple Inc.

Sunnyvale (CA)

On-site

USD 166,560 - 277,600

Full time

14 days+
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Job summary

Apple Inc. in Sunnyvale is seeking a data engineer to build scalable data infrastructure powering health research. The role focuses on developing high-performance data pipelines and processing systems to support large-scale health studies and analytics.

You will design ETL pipelines with Spark, work with researchers and product teams, and contribute to a health data platform across studies, driving standardization and reusability.

Qualifications

  • BS degree in CS, Engineering, Data Science or related field.
  • 3+ years building and maintaining data pipelines or distributed data systems.
  • Proficiency in Python and writing clean, high-performance code.
  • Familiarity with SQL for querying and transforming data.
  • Hands-on experience with distributed computing frameworks like Apache Spark.
  • Experience with ETL development, data modeling, and data transformation workflows.

Responsibilities

  • Design and develop scalable, parallelized data systems for time-series health data.
  • Implement robust ETL pipelines using Spark to ingest and transform data formats.
  • Collaborate with study stakeholders to gather requirements and support study design.
  • Build tools and dashboards to visualize health data and support analysis.
  • Work with cross‑functional teams to support design, testing, and deployment.

Skills

Python
SQL
Spark
ETL development

Education

BS in Computer Science/Engineering/Data Science

Tools

Docker
Kubernetes
Airflow

Job description

Apple Inc. in Sunnyvale is seeking a data engineer to build scalable data infrastructure powering health research. The role focuses on developing high-performance data pipelines and processing systems to support large-scale health studies and analytics.

You will design ETL pipelines with Spark, work with researchers and product teams, and contribute to a health data platform across studies, driving standardization and reusability.

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