Senior Engineering Manager, Machine Learning Data Enablement

What you'll do

• Lead a high-performing team across data integration, quality, metadata, and ML-critical infrastructure for training and inference.
• Execute technical strategies to increase data evaluation velocity and reduce time-to-production for high-value data sources.
• Develop robust data quality and reconciliation frameworks including monitoring, drift detection, and production checks.
• Establish company-wide data contracts and SLAs to ensure clear ownership and standards for ML-critical datasets.
• Accelerate third-party data onboarding by operationalizing standardized vendor intake and automated integration workflows.
• Improve internal data discoverability by enhancing metadata coverage, lineage standards, and ownership contracts.
• Partner cross-functionally with ML, Product, and Data Platform teams to transform data into a competitive advantage.

What we're looking for

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent).
• At least 8 years of engineering experience including at least 3 years of direct people management experience.
• Experience owning production data pipelines that enable both offline training and online inference.
• Proven experience building and scaling data systems using modern stacks like Databricks/Spark, Python, SQL, AWS, and distributed systems.
• Demonstrated ownership of complex cross-functional initiatives involving engineering, ML, and business stakeholders.
• 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 teams (preferred).
• Experience with feature stores, real-time feature delivery, lakehouse architecture, or fintech environments (preferred).