AI-First Data Engineering Manager: Snowflake & Airflow Lead

Commerce; Austin, Northern (TX, KY); Hybrid; USD 150,000 - 220,000; Full time

Job summary

Commerce is seeking an AI-first Data Engineering Manager to lead the Enterprise Data Engineering team in Austin. You will own the Snowflake- and Airflow-based platforms powering reporting, analytics, AI, and data products, while guiding a team that treats AI agents as first-class teammates in the data lifecycle.

This hands-on leadership role requires shipping production data pipelines, mentoring engineers, and embedding agentic tooling to improve quality, speed, and governance across projects.

Qualifications

• 7+ years in data engineering with 3+ years in people leadership.
• Hands-on production experience with Snowflake and Apache Airflow.
• Strong SQL and Python skills; able to review or write pipeline code.
• Experience with distributed teams across time zones.
• Bachelor's degree in Computer Science or Engineering, or equivalent.

Responsibilities

• Lead, mentor, and grow a team of data engineers.
• Own the technical roadmap and architecture for Enterprise Data platform (Snowflake, Airflow).
• Embed agentic tools into daily workflow for pipeline development and quality checks.
• Partner with Analytics, Data Science, Product, and business stakeholders.
• Establish data quality, observability, and cost-efficiency standards.
• Drive modernization of data pipelines and evaluate new tooling.

Skills

Data engineering

Team leadership

SQL

Python

Data governance

Education

Bachelor's in CS/Engineering

Tools

Snowflake

Apache Airflow

dbt

Fivetran

Airbyte

Claude Code

GitHub Copilot

Snowflake CoCo

Job description

Commerce is seeking an AI-first Data Engineering Manager to lead the Enterprise Data Engineering team in Austin. You will own the Snowflake- and Airflow-based platforms powering reporting, analytics, AI, and data products, while guiding a team that treats AI agents as first-class teammates in the data lifecycle.

This hands-on leadership role requires shipping production data pipelines, mentoring engineers, and embedding agentic tooling to improve quality, speed, and governance across projects.