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.
Required Qualifications and Experience
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.
Preferred Qualifications and 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.
