Senior Engineering Manager - ML Data Enablement

The Team

Upstart’s ML Data Enablement team is a platform team with end-to-end ownership 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

• Set and execute the technical strategy aligned to measurable north star metrics such as increasing data evaluation velocity and reducing time to production for high-value data sources.
• 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.
• 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.
• Unlock internal data for ML innovation by improving metadata coverage, lineage standards, ownership contracts, and ML discoverability across high-impact internal domains
• 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.
Build and lead a high-performing team spanning data integration, data quality, metadata, and ML-critical data infrastructure, including standing up new dedicated integration capacity where needed.

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
• Proven experience building and scaling data platforms that support machine learning workflows (offline training and online inference).
• Demonstrated ownership of complex, cross-functional initiatives spanning engineering, ML, and business stakeholders.
• Experience designing and enforcing data quality frameworks, SLAs, and observability for production systems.
• Strong technical foundation in modern data stacks (e.g., Databricks/Spark, Python, SQL, AWS, streaming systems, orchestration frameworks) and distributed systems architecture.

Preferred qualifications:

• 10+ years of experience in data engineering, ML platform, or ML data platform roles, with 5+ years managing engineering teams.
• Experience managing data platform or infrastructure teams in high-growth consumer tech or fintech environments.
• Strong knowledge of Lakehouse architecture, and big data processing frameworks.
• Familiarity with DevOps and infrastructure-as-code practices (Kubernetes, Terraform, CI/CD).
• Advanced degree in Computer Science, Statistics, or a related technical field.
• Ability to translate complex technical concepts into business value and influence cross-functional strategies.