Senior Engineering Manager, Upstart

• 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

Desirable

• 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
• 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