Senior Manager, ML Data Enablement

About Upstart

Upstart is the leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than 80% of borrowers are approved instantly, with zero documentation to upload.

Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California; Columbus, Ohio; and Austin, Texas.

Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you!

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.