Engineering Manager, Data Platform & ML Ops
New
United StatesFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 5+ years of professional experience in data engineering, ML engineering, or closely related software engineering roles; 2+ years of experience managing engineering teams
- Required Skills
- AWSMachine LearningSnowflakeClickhouseData engineeringSaaSBigQueryDatabricksMLOps
Requirements
- Minimum 2+ years of experience managing engineering teams in data, ML, or platform domains.
- 5+ years of professional experience in data engineering, ML engineering, or closely related software engineering roles.
- Strong technical foundation across data infrastructure and machine learning systems in SaaS environments.
- Hands-on familiarity with ML Ops workflows including training pipelines, deployment, and monitoring.
- Experience working with modern data platforms such as ClickHouse, Databricks, Snowflake, or BigQuery.
- Proficiency with cloud infrastructure, particularly AWS-based data and ML systems.
- Proven ability to lead high-reliability engineering teams delivering production-grade data products.
- Strong communication, leadership, and cross-functional collaboration skills.
Responsibilities
- Lead, mentor, and develop a team of data platform and ML Ops engineers, fostering technical excellence and career growth.
- Own the architecture, reliability, and scalability of the internal data warehouse powering analytics and product intelligence.
- Oversee the end-to-end ML Ops lifecycle including experimentation, training pipelines, deployment, and production monitoring.
- Guide technical architecture decisions and review complex engineering proposals across data and ML systems.
- Collaborate with data scientists, product managers, and engineering leaders to translate data initiatives into business impact.
- Define and evolve engineering standards, tooling, and best practices across data and ML infrastructure.
- Drive continuous improvement in system performance, reliability, and operational maturity.
- Ensure delivery of high-impact data products that support fraud detection and identity intelligence systems.
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