The Team

Upstart’s ML Data Enablement team is a platform team with end-to-end ownership (from source to inference) of the data lifecycle that powers all ML models across external vendors and internal datasets. The team’s mission is to make it dramatically easier for ML teams to discover, evaluate, trust, and productionize high-impact data. — with particular emphasis on accelerating new third-party data onboarding and unlocking under-leveraged internal data.

The team builds scalable infrastructure, standardized workflows, and quality guarantees that reduce integration time, increase evaluation velocity, and enforce strong ownership and SLAs across the ML data lifecycle.

As the Sr. Engineering Manager - ML Data Enablement, you will lead this organization and define the strategy, operating model, and execution roadmap that increases data evaluation velocity and reduces time-to-production for high-value data sources. You will partner cross-functionally with ML, ML Platform, Procurement, Data Platform, and product engineering teams to transform data from a bottleneck into a durable competitive advantage.

How you’ll make an impact

• • Build and lead a high-performing team spanning data integration, data quality, metadata, and ML-critical data infrastructure for online inference and offline training, including standing up new dedicated integration capacity where needed.
  • Set and execute the technical strategy aligned to measurable north star metrics such as increasing data evaluation velocity and reducing time to production.
  • Drive robust data quality and reconciliation frameworks, including retro vs. production checks, ingress-level monitoring, and drift detection to prevent launch issues and downstream model degradation.
  • Champion a company-wide shift toward data contracts and SLAs, ensuring data producers adopt clear ownership, quality standards, and monitoring practices for ML-critical datasets.
  • Establish clear end-to-end ownership across the third-party and internal data lifecycle, eliminating fragmented workflows and implicit accountability.
  • Accelerate third-party data onboarding by operationalizing standardized vendor intake, secure retro ingestion, templated integrations, and configurable microservices that reduce engineering lift and cycle time.
  • Unlock internal data for ML innovation by improving metadata coverage, lineage standards, ownership contracts, and ML discoverability across high-impact internal domains

What we’re looking for

Minimum requirements

• Bachelor’s degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of engineer experience, including at least 3 years of direct people management experience
• Owned production data pipelines that enable both offline training and online inference
• Proven experience building and scaling data systems in modern stacks (e.g., Databricks/Spark, Python, SQL, AWS, streaming systems, orchestration frameworks) and distributed systems architecture.
• Demonstrated ownership of complex cross-functional initiatives spanning engineering, ML, and business stakeholders, including delivery under peer pushback and dependency negotiation.
• Experience designing and enforcing data quality frameworks and observability for production systems, including reconciliation, drift detection, and incident/postmortem operating loops.

Preferred qualifications

• 10+ years in data engineering AND ML platform OR ML data platform roles, with 5+ years managing engineering teams. (strongly preferred(
• Experience with feature stores and real-time feature delivery or equivalent feature transformation interfaces used in inference.
• Strong knowledge of lakehouse architecture and big data processing frameworks.
• Familiarity with DevOps and infrastructure-as-code practices (Kubernetes, Terraform, CI/CD).
• Experience in fintech or other regulated environments where explainability, auditability, and controls matter.
• Ability to translate complex technical tradeoffs into business impact and influence cross-functional strategy.