Senior Engineering Manager – Machine Learning Data Enablement

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee the ML Data Enablement organization

• Develop the team’s strategy, operating model, and execution roadmap

• Create and manage a high-performing team focused on data integration, data quality, metadata, and ML-critical data infrastructure for both online inference and offline training

• Implement dedicated integration capabilities where necessary

• Define and execute technical strategy based on metrics such as data evaluation speed and time to production

• Lead initiatives for data quality and reconciliation frameworks, including retro-versus-production checks, ingress-level monitoring, and drift detection

• Advocate for company-wide data contracts and SLAs for ML-critical datasets

• Establish complete ownership over third-party and internal data lifecycles

• Streamline third-party data onboarding via standardized vendor intake, secure retro ingestion, templated integrations, and configurable microservices

• Enhance metadata coverage, lineage standards, ownership contracts, and ML discoverability across internal data domains

• Collaborate with ML, ML Platform, Procurement, Data Platform, and product engineering teams

⛳️ Requirements

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or its equivalent plus 8 years of engineering experience

• Minimum of 3 years of direct people management experience

• Experience managing production data pipelines that support offline training and online inference

• Proficiency in building and scaling data systems using technologies like Databricks/Spark, Python, SQL, AWS, streaming systems, and orchestration frameworks

• Familiarity with distributed systems architecture

• Proven track record of managing complex cross-functional initiatives involving engineering, ML, and business stakeholders

• Capability to deliver results despite peer pushback and to negotiate dependencies

• Experience in designing and enforcing data quality frameworks and production observability

• Knowledge of reconciliation, drift detection, and incident/postmortem operating procedures

• Preferred: 10+ years in data engineering and ML platform or ML data platform roles, with 5+ years in leadership positions

• Preferred: Experience with feature stores and real-time feature delivery or equivalent inference feature transformation interfaces

• Preferred: Understanding of lakehouse architecture and big data processing frameworks

• Preferred: Familiarity with Kubernetes, Terraform, and CI/CD methodologies

• Preferred: Experience in fintech or regulated environments

• Preferred: Ability to translate technical tradeoffs into business impact and influence cross-functional strategies

🏝️ Benefits

• Competitive compensation, including base salary, 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) offering discounted stock purchase options for eligible employees (US only)

• Comprehensive health coverage, including medical, dental, vision, and wellness resources for US employees, as well as supplemental health coverage for Canada

• Contributions to Health Savings Accounts for eligible plans (US only)

• Life insurance and disability coverage

• Paid time off, sick leave, and company holidays

• Paid family and parental leave

• Family-focused benefits supporting fertility, parenthood, and caregiving

• Employee Assistance Program (EAP) providing 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