Engineering Manager, Data Platform & ML Ops

Fingerprint empowers developers to stop online fraud at the source. They are seeking an Engineering Manager to lead their Data Platform & ML Ops team, responsible for the data foundation and ML Ops lifecycle, while fostering a culture of high performance among engineers.

Responsibilities:

• Lead and mentor a team of 4-6 engineers spanning data platform and ML operations
• Own the reliability, scalability, and evolution of Fingerprint's internal data warehouse — the foundation for business analytics and a direct input to our flagship identification and smart signals products
• Oversee the full ML Ops lifecycle end-to-end: experimentation, training pipelines, model deployment, and production monitoring
• Provide technical leadership by collaborating with senior engineers, guiding architecture decisions, and reviewing complex technical proposals
• Work closely with data scientists, product managers, data analysts and engineering leads to translate data and ML investments into measurable product outcomes
• Coach and support engineer growth, promoting continuous learning across a fast-moving data and ML landscape
• Define and evolve platform standards, tooling, and best practices across both domains

Requirements:

• Minimum of 2 years of experience leading data engineering, ML engineering, or platform teams in an agile environment
• At least 5 years of professional experience in data engineering, ML engineering, or adjacent software engineering, particularly within SaaS. Hands-on experience in both data infrastructure and ML systems is a must — you don't need to be an expert in both, but you should be technically credible on both sides of the house
• Strong technical background across data infrastructure and ML systems
• Experience managing engineers across multiple technical disciplines
• Proven ability to lead teams shipping high-reliability data products that prioritize quality and user impact
• Demonstrated success driving change and innovation in fast-paced, scaling environments
• Experience leading teams in a startup or high-growth environment
• Familiarity with analytical storage systems such as ClickHouse, DataBricks, Snowflake, or BigQuery
• Experience with ML lifecycle tooling — training pipelines, model serving, and production monitoring
• Experience with AWS and cloud-based data and ML infrastructure