Fingerprint
Engineering Manager, Data Platform & ML Ops
RemoteRemotePosted 1 month ago$159,000 – $215,000
Full TimeSeniorRemote
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Job Description
Fingerprint empowers developers to stop online fraud at the source.We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients ra
Key Highlights
- 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.
Qualifications
Required Qualifications
- 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.
- 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.
- 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.
Skills & Technologies
RedisAgileAWS
About the Company
Fingerprint
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Job Details
Employment Type
Full Time
Experience Level
Senior
Salary Range
$159,000 – $215,000
Location
Remote
Work Mode
Remote
Posted
1 month ago