Sr Staff Engineer - Engineer

The Mobility + Platform data org is the foundational layer that every Mobility product (Rider, Driver, Fares, Matching, Fulfillment) builds on. We operate a hub-and-spoke data model: a central core team (the data engineering Guardians) owns the tracks so the trains can run fast and safely, while embedded data engineers sit inside Fulfillment, Fares, Forge, and other verticals to serve local needs without duplicating foundational work.

Our mission is to turn millions and billions of raw, fast-moving event streams from multiple systems into a single, trustworthy Mobility + Platforms data lakehouse that every analyst, data scientist, and ML engineer etc at Uber depends on.

A Senior Staff Data Engineer on this team is the person trusted to lead these initiatives for the Mobility Org at Uber.

• As a Senior Staff Data Engineer, you are an organization-level technical leader, not just for one team, but for the Mobility + Platform data ecosystem as a whole. You set multi-half-to-multi-year direction, force-multiply dozens of engineers across teams you don't directly manage, and are trusted to point Uber's technical investment in the right direction with real autonomy.
• Architect the data lake/lakehouse strategy for Mobility + Platform
• Own data modeling and schema design for core mobility entities that dozens of downstream teams build on; this role is explicitly weighted toward deep data modeling and SQL mastery over general-purpose application coding.
• Set the technical vision and strategy for an entire portfolio of data platform investments (governance, schema registry, pipeline health/SLAs, backfill tooling, validation frameworks) rather than executing a single project; identify ambiguous, org-wide data problems before they're obvious to others.
• Build and scale the pipelines and infrastructure (batch and streaming) that keep petabyte-scale mobility data fresh, reliable, cost-efficient, and auditable.
• Drive data governance and quality as a first-class engineering discipline.
• Mentor and uplevel Staff and front-line engineers and EMs across multiple teams; act as a technical thought partner
• Partner cross-functionally with Mobility Science, Fares, Matching, Fulfillment, and platform teams to align the mesh of embedded and central data engineering work.
• Represent data engineering in org-wide planning, advocating for and securing investment in projects with outsized, multi-team business impact; think in terms of large-scale transitive impact, not single-team wins.

• 10-15+ years of overall software/data engineering industry experience, with substantial, demonstrable hands-on depth specifically in data engineering (data modeling, pipeline architecture, warehousing) built up within that tenure.
• Deep expertise in data modeling and schema design for large-scale, multi-domain data lakes/lakehouses.
• Expert-level SQL and strong command of distributed data processing and storage systems (e.g., Spark, Hive, Presto, Kafka/Flink, Airflow, or equivalent cloud-scale tools).
• Proven track record of owning technical strategy for an organization, not just a single team: defining multi-half-to-multi-year roadmaps and influencing outcomes across teams you don't directly manage.
• Demonstrated ability to lead through influence: building trusted relationships across engineering, product, and data science leadership, and aligning disparate teams around a shared data strategy.
• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.

• Experience designing or operating a certified/golden-dataset governance model (e.g., medallion/L0-L3 architectures, source-of-truth certification programs) at a large tech company.
• Experience partnering with or building metrics-layer platforms (semantic layers, feature stores for ML) on top of a data lake.
• A track record of mentoring Staff-level engineers and engineering managers, operating as a leader of leaders rather than solely a hands-on contributor.
• Experience driving cost optimization and performance tuning for petabyte-scale batch and streaming data platforms.