Senior Engineering Manager – Machine Learning Data Enablement

Upstart

. Lead the ML Data Enablement organization .

Posted 9/22/2026full-timeRemote • United StatesSenior💰 $195,000 - $270,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading ML Data Enablement initiatives, focusing on data integration, quality frameworks, and infrastructure for machine learning. Proven ability to manage high-performing teams and drive technical strategies that enhance data evaluation and production timelines.

Highest-signal resume keywords

Data Pipeline OwnershipData Quality FrameworksPeople ManagementDistributed Systems ArchitectureData Integration Strategies

ATS Keywords

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Hard Skills

Data EngineeringMachine LearningData QualityData IntegrationPythonSQLDatabricksSparkAWSStreaming Systems

Soft Skills

LeadershipNegotiationCross-Functional CollaborationProblem Solving

Tools & Technologies

KubernetesTerraformCI/CDFeature StoresBig Data Processing Frameworks

Industry Keywords

FintechRegulated EnvironmentsLakehouse Architecture

Tech Stack

Tools & technologies

AWSDistributed SystemsKubernetesMicroservicesPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Lead the ML Data Enablement organization
Define the team’s strategy, operating model, and execution roadmap
Build and lead a high-performing team spanning data integration, data quality, metadata, and ML-critical data infrastructure for online inference and offline training
Establish dedicated integration capacity where needed
Set and execute technical strategy against metrics such as data evaluation velocity and time to production
Drive data quality and reconciliation frameworks, including retro-versus-production checks, ingress-level monitoring, and drift detection
Champion company-wide data contracts and SLAs for ML-critical datasets
Establish end-to-end ownership across third-party and internal data lifecycles
Accelerate third-party data onboarding through standardized vendor intake, secure retro ingestion, templated integrations, and configurable microservices
Improve metadata coverage, lineage standards, ownership contracts, and ML discoverability across internal data domains
Partner with ML, ML Platform, Procurement, Data Platform, and product engineering teams

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or its equivalent plus 8 years of engineering experience
At least 3 years of direct people management experience
Owned production data pipelines enabling offline training and online inference
Experience building and scaling data systems using technologies such as Databricks/Spark, Python, SQL, AWS, streaming systems, and orchestration frameworks
Experience with distributed systems architecture
Ownership of complex cross-functional initiatives spanning engineering, ML, and business stakeholders
Experience delivering under peer pushback and negotiating dependencies
Experience designing and enforcing data quality frameworks and production observability
Experience with reconciliation, drift detection, and incident/postmortem operating loops
Preferred: 10+ years in data engineering and ML platform or ML data platform roles, with 5+ years managing engineering teams
Preferred: Experience with feature stores and real-time feature delivery or equivalent inference feature transformation interfaces
Preferred: Knowledge of lakehouse architecture and big data processing frameworks
Preferred: Familiarity with Kubernetes, Terraform, and CI/CD
Preferred: Experience in fintech or regulated environments
Preferred: Ability to translate technical tradeoffs into business impact and influence cross-functional strategy

Benefits

Comp & perks

Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually
Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees (US only)
Comprehensive health coverage, including medical, dental, vision, and wellness resources for US and supplemental health coverage for Canada
Health Savings Account contributions for eligible plans (US only)
Life insurance and disability coverage
Paid time off, sick leave, and company holidays
Paid family and parental leave
Family-centered benefits supporting fertility, parenthood, and caregiving
Employee Assistance Program (EAP) offering mental health support and life-centered resources
Financial planning tools and financial concierge service (US only)
Annual wellness allowance
Annual productivity allowance for relevant tools and resources
Team events, all-company updates, and employee resource groups (ERGs)
Catered lunches and fully stocked micro-kitchens at offices in the Bay Area, Austin, Columbus, and New York City