Data Platform Engineering Lead

The opportunityBuild the Backbone of AI: You won't just move data; you will architect the Feature Platform and Streaming flows that directly feed our ML models for real-time decisioning.

Lead a High-Caliber Team: You are inheriting an established team of senior engineers. Your focus will be on architecture, strategy, and removing roadblocks rather than micromanaging.

Impact at Massive Scale: Operate at the edge of what’s possible with petabytes of data, massive compute, and complex cost-optimization challenges on AWS.

Redefine the "Lakehouse": Move beyond standard implementations by maturing our Apache Iceberg strategy and our in-house Iceberg-Manager to automate lifecycle management.

Drive Engineering Culture: Champion "Platform as a Product"—building golden paths and self-service tooling that allow engineers and data scientists to ship faster and safer.

What you'll be doing

• Team & Tech Leadership: Lead, coach, and unblock a senior Data Platform team; partner with ML Engineers and Data Scientists to align the roadmap with our AI goals.
• Feature Platform & Streaming: Drive the adoption of Apache Flink and unify online/offline data patterns to ensure low-latency, consistent feature serving for experimentation.
• Compute & Query Engine Architecture: Oversee the performance and cost-tuning of our compute layers (EMR on EKS, Spark, Trino, Druid), ensuring fast analytics and efficient batch processing.
• Reliability & Observability: Own the platform’s SLOs; drive deep analysis of metrics and logs, and promote stability (using Prometheus, VictoriaMetrics).
• Strategic FinOps: Treat cost as an engineering constraint. Utilize Spot instances, Karpenter, and S3 lifecycle policies to maximize "cost per insight."

What we're looking for

• 2+ years leading engineers (Manager or heavy Tech Lead) in a high-scale environment; you know how to nurture senior talent and manage stakeholders.
• 5+ years total in Data Infrastructure/DataOps roles including on-call.
• Deep production experience with Apache Iceberg (partitioning, compaction, schema evolution) and modern file formats (Parquet/Avro).
• Proven ownership of cloud-native data platforms on AWS: S3, EMR (preferably EMR on EKS), IAM, Glue/Data Catalog, Athena.
• Data for ML Experience: You understand the lifecycle of data as it pertains to ML models—feature stores, training/serving skew, and online retrieval.
• Hands-on depth in Compute Engines: You understand the internals of at least two: Spark, Trino, Flink, or Druid.
• Strong foundational understanding of Kubernetes (EKS) in stateful contexts.

You might also have

• Experience building feature platforms (feature definitions, materialization, serving, and online/offline consistency).
• Familiarity with Airflow (at scale) or Argo Workflows.
• Exposure to cross-cloud interoperability (BigQuery, BigLake) or operational databases (Aurora MySQL).
• Experience with OLAP alternatives like ClickHouse, StarRocks, or Pinot.
• A strong FinOps mindset (Savings Plans, attribution, Spot strategies).
• Data quality, lineage, and cataloging practices in large orgs.
• IaC (Terraform/CloudFormation)

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Life at UnityWe offer a wide range of benefits designed to support employees' well-being and work-life balance. You can read more about them on our career page.

Unity [NYSE: U] is the leading platform to create and grow games and interactive experiences. Creators, ranging from game developers to artists, architects, automotive designers, infrastructure experts, filmmakers, and more, use Unity to bring their imaginations to life across multiple platforms, from mobile, PC, and console, to spatial computing.

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