Senior Engineering Manager - Machine Learning Data Enablement

Upstart is a leading AI lending marketplace dedicated to reducing the cost and complexity of borrowing for all Americans. As the Senior Engineering Manager for Machine Learning Data Enablement, you will lead a high-performing team to enhance data evaluation velocity and reduce production time for high-value data sources, ensuring robust data quality and ownership across the data lifecycle.

Responsibilities:

• 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

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
• 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