# Engineering Manager, Data Platform & ML Ops

**Company:** [Fingerprint](https://hotfix.jobs/companies/fingerprint)
**Location:** Remote
**Role:** Engineering Management
**Salary:** $159k – $215k/yr
**Experience:** 8+ years
**Skills:** ClickHouse, Databricks, dbt, prefect, datahub, aws sagemaker, AWS, Snowflake, BigQuery, MLOps, Data Engineering, ml engineering
**Posted:** 2026-08-05

> Leads a 4–6-person team responsible for data platform reliability, analytics infrastructure, and the end-to-end MLOps lifecycle. Requires 5+ years of relevant engineering experience, 2+ years leading technical teams, and credibility across both data infrastructure and ML systems.

## Job Description

## 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.

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