Senior Engineering Manager, Machine Learning Data Enablement

What you'll do

• Build and lead a high-performing team spanning data integration, data quality, metadata, and ML-critical data infrastructure.
• Set and execute a technical strategy to increase data evaluation velocity and reduce time-to-production for high-value data sources.
• Drive robust data quality and reconciliation frameworks, including ingress-level monitoring and drift detection.
• Champion a company-wide shift toward data contracts and SLAs to ensure clear ownership and quality standards for ML datasets.
• Accelerate third-party data onboarding by operationalizing standardized vendor intake and configurable microservices.
• Improve metadata coverage, lineage standards, and ML discoverability for internal data domains.
• Establish end-to-end ownership across the third-party and internal data lifecycle to eliminate fragmented workflows.

What we're looking for

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
• At least 8 years of engineering experience.
• At least 3 years of direct people management experience.
• Experience owning production data pipelines for both offline training and online inference.
• Experience building and scaling data systems using Databricks/Spark, Python, SQL, AWS, streaming systems, and orchestration frameworks.
• Experience designing and enforcing data quality frameworks and observability for production systems.
• 10+ years in data engineering and ML platform roles, with 5+ years managing engineering teams (preferred).
• Experience with feature stores, real-time feature delivery, lakehouse architecture, or DevOps/infrastructure-as-code (preferred).