Senior Data Engineering Lead — Capacity Data Lake

Apple Inc.; Seattle (WA); On-site; USD 226,000 - 338,000; Full time

Job summary

Apple is seeking a Senior Data Engineering Manager to lead the Capacity Engineering data team in Seattle. You will own the capacity data lake and pipelines, turning demand, supply, utilization, and cost signals into trusted analytics for capacity decisions.

You will set the technical direction, ensure data quality, govern data, and collaborate with forecasting, platform, and finance teams, while remaining an active technical contributor on architecture and data models.

Qualifications

• BS/MS in Computer Science, Engineering, or equivalent practical experience.
• 5+ years of engineering leadership experience.
• Hands-on data engineering with data lakes, models and ETL pipelines.
• Experience with Spark, Databricks, Hadoop, and cloud data stores.
• Ability to remain technically hands-on and lead architecture reviews.
• Strong data-analysis instincts to derive business insight.
• Excellent written and verbal communication; able to present to engineers and leadership.
• Willingness to be based in Seattle and lead a distributed team.

Responsibilities

• Lead the capacity data lake and pipelines, owning data governance and accessibility.
• Set technical direction for ingestion, modeling, governance, and serving.
• Be a visible technical contributor, deep-diving into architecture and data quality.

Skills

Hands-on data engineering

Data modeling

Data quality & governance

Architecture reviews

Leadership & mentoring

Communication to leadership

Education

BS/MS in Computer Science, Engineering, or equivalent practical experience

Tools

Spark

Databricks

Hadoop

Aurora

S3-compatible storage

Job description

Apple is seeking a Senior Data Engineering Manager to lead the Capacity Engineering data team in Seattle. You will own the capacity data lake and pipelines, turning demand, supply, utilization, and cost signals into trusted analytics for capacity decisions.

You will set the technical direction, ensure data quality, govern data, and collaborate with forecasting, platform, and finance teams, while remaining an active technical contributor on architecture and data models.