Engineering Manager, Data Platform & ML Ops

Responsibilities

• Lead and mentor a team of 4–6 engineers across data platform and ML operations.
• Own the reliability, scalability, and evolution of the internal data warehouse supporting business analytics and product analytics.
• Oversee the full MLOps lifecycle, including experimentation, training pipelines, model deployment, and production monitoring.
• Provide technical leadership by collaborating with senior engineers, guiding architecture decisions, and reviewing complex technical proposals.
• Partner with data scientists, product managers, data analysts, and engineering leads to translate data and ML investments into measurable product outcomes.
• Coach engineers and support their growth while promoting continuous learning.
• Define and evolve platform standards, tooling, and best practices across data and ML operations.

Requirements

• At least 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 in SaaS.
• Hands-on experience with both data infrastructure and ML systems.
• Strong technical background across data infrastructure and ML systems.
• Experience managing engineers across multiple technical disciplines.
• Proven ability to lead teams shipping highly reliable data products that prioritize quality and user impact.
• Demonstrated success driving change and innovation in fast-paced, scaling environments.

Preferred Qualifications

• 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, including training pipelines, model serving, and production monitoring.
• Experience with AWS and cloud-based data and ML infrastructure.

Technologies

• Data platform: ClickHouse, Databricks, dbt, Prefect, DataHub
• MLOps: AWS SageMaker
• Infrastructure: AWS

Compensation

• US-based cash compensation range: $159,000–$215,000.
• Compensation may vary based on experience, education, certifications, skills, training, and market conditions.