Data Engineer

The role

This position sits in the Analytics Data Engineering group inside Data Science and AI Platform. The team is constructing its data platform largely from scratch rather than maintaining legacy systems, so the work centers on greenfield design and newer big-data tooling. The engineer will shape architecture and data assets that serve several business units.

What you would do

• Specify the core company datasets and warehouse schemas, and write Spark and SparkSQL jobs that load them
• Set up data integration patterns, quality checks, and assess open-source or commercial lineage tooling
• Partner with business units and engineering leads to chart a durable, dependable platform roadmap
• Own architecture for several big initiatives, weighing cost and operational trade-offs
• Gather data requirements from engineers, product owners, and scientists and communicate insights clearly
• Build and ship layered dimensional models and dashboards that serve multiple products
• Refine pipelines and internal frameworks so teammates can produce data artifacts more easily
• Take occasional on-call rotations to resolve incidents and keep customer experience stable

What they asked for

• Five or more years building in Spark, Python, Java, C++, or Scala
• Five or more years working with SQL
• Five or more years on schema design, dimensional modeling, and medallion-style layering
• Hands-on experience with Databricks and lake-based architectures for big data processing
• Strong strategic product sense and communication skills to drive cross-team impact through data
• Bachelor's in computer science or a closely related technical discipline, or equivalent hands-on experience
• Track record designing, building, and running data processing systems

Nice to have

• Seven or more years of SQL experience
• Seven or more years in schema design and medallion architecture
• Familiarity with Airflow or comparable orchestration tools
• Experience creating data quality monitors with Monte Carlo or similar products