Senior Engineering Manager - Machine Learning Data Enablement

Upstart is a leading AI lending marketplace on a mission to reduce the cost and complexity of borrowing for all Americans. The Senior Engineering Manager - ML Data Enablement will lead a team responsible for optimizing the data lifecycle that supports machine learning models, enhancing data evaluation velocity, and reducing time-to-production for high-value data sources.

Responsibilities:

• 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

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
• 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