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.
Tech Stack
AWS
Distributed Systems
Microservices
Python
Spark
SQL
Benefits
Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
Generous 401(k) plan with Upstart matching $2 for every $1 contributed, up to $15,000 per year
Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees
Affordable medical, dental, and vision coverage, with multiple plan options
Upstart covers 90% to 100% of the cost depending on the plans you choose
Health Savings Account contributions from Upstart for eligible plans
Income protection benefits, including company-paid Basic Life, AD&D, and Short
and Long-Term Disability coverage, with options to purchase supplemental coverage
Paid time off, sick and safe time, and company holidays
Paid family and parental leave to support caregiving and major life moments
Family-centered benefits through Carrot and Cleo, supporting fertility, parenthood, and caregiving
Employee Assistance Program (EAP) offering mental health support and life-centered resources
Financial wellness resources, including access to financial planning tools and a financial concierge service
Annual wellness allowance to support your physical and emotional well-being and personal development, based on what matters most to you
Annual productivity allowance to invest in relevant tools and resources you need to do your best work, no matter where you work from
Connection and community through team events and onsites, all-company updates, and employee resource groups (ERGs)
Onsite perks, including catered lunches and fully stocked micro-kitchens when working from one of our four offices, located in the Bay Area, Austin, Columbus, and New York City (opening Summer 2026!).