Senior Staff Data Engineer - Data & ML Platform

What You'll Accomplish

Set the technical vision for the data platform: Own the long-term architectural direction for how streaming and batch systems, data models, and serving layers fit together. Make the architectural decisions that other teams and engineers build on — balancing reliability, performance, cost, and long-term maintainability across the platform.

Build at the intersection of data and ML platform: Design the infrastructure that connects the data platform to ML workloads — feature pipelines, feature stores, and serving layers. Partner with Data Science to ensure the data platform produces ML-ready data and supports model training and inference workflows reliably.

Raise the engineering bar across the organization: Set standards that extend beyond your immediate team — data modeling patterns, schema governance, testing practices, pipeline reliability, and code quality. Mentor senior engineers, influence engineering culture, and be the technical authority the broader R&D organization looks to on data platform decisions.

Drive cross-organizational technical initiatives: Lead complex initiatives that span multiple teams, services, and domains. Define data contracts with upstream services, drive schema evolution strategies, and resolve systemic technical friction between data producers and consumers across the company.

Own platform reliability and operational excellence: Drive the reliability posture of the most critical data systems. Lead improvements in observability, data quality, incident response, and cost efficiency at a platform level — making the data foundation trustworthy enough that every team in the organization can build confidently on top of it.

Basic Qualifications

• Bachelor’s Degree (or equivalent) in Computer Science, Engineering, or a related technical field
• 10+ years of hands-on data engineering experience, with a significant portion spent in platform or infrastructure roles building systems that other teams depend on
• Experience architecting data systems across batch and streaming paradigms, including technologies such as Kafka, Flink, Spark, or equivalent
• Strong proficiency in Python and SQL, with deep experience in distributed data processing frameworks and data platform design
• Data and ML platform crossover: You've built or contributed to ML platform infrastructure — feature pipelines, feature stores, model serving, or MLOps tooling — as a natural extension of your data engineering work
• Track record of setting technical direction across an organization — driving alignment across multiple teams, making architectural decisions with broad impact, and delivering outcomes without formal authority
• Demonstrated experience mentoring senior engineers and influencing engineering culture and standards beyond your immediate team

Preferred Qualifications

• Built in a growth-stage environment: Your strongest work was at a mid-sized or scaling company where you had to make foundational architectural decisions
• Deep data modeling and governance instincts: You care about schema design, data contracts, and data quality as much as you care about pipeline throughput
• Product and business awareness: You connect your technical work to the problems the business is trying to solve
• Operational rigor in regulated environments: Experience in HIPAA, SOC 2, or similarly regulated environments is a plus
• Experience with Databricks ecosystem: Delta Lake, MLflow, Unity Catalog
• AI-forward engineering practices: You actively use AI-assisted development tools