Senior Data Engineering Manager at Upstart

, , United States -

Full Time

Start Date

Immediate

Expiry Date

27 May, 26

Salary

270000.0

Posted On

26 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Platforms, Machine Learning Workflows, Data Quality Frameworks, SLAs, Observability, Databricks, Spark, Python, SQL, AWS, Streaming Systems, Orchestration Frameworks, Distributed Systems Architecture, Lakehouse Architecture, DevOps, Terraform

Industry

Financial Services

Description

About Upstart At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence. As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 1,800 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress. We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you. If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you. 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. At Upstart, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, Upstart uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k). United States | Remote - Anticipated Base Salary Range $195,000—$270,000 USD What you'll love At Upstart, our benefits are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect: 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!). Upstart is a proud Equal Opportunity Employer. Just as we are dedicated to improving access to affordable credit for all, we are committed to inclusive and fair hiring practices. If you require reasonable accommodation in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please email candidate_accommodations@upstart.com https://www.upstart.com/candidate_privacy_policy

Responsibilities

This role involves leading the ML Data Enablement organization to define strategy and execution roadmaps that accelerate data evaluation velocity and reduce time-to-production for high-value data sources. Key activities include establishing end-to-end ownership across the data lifecycle, accelerating third-party data onboarding, and driving robust data quality and reconciliation frameworks.