AI-First Data Engineering Lead

BigCommerce, Inc.; Austin (TX); Hybrid; USD 150,000 - 220,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

BigCommerce, Inc. is seeking an AI-first Data Engineering Manager to lead Enterprise Data, built on Snowflake and orchestrated with Airflow. You will grow a team, own the roadmap, and embed agentic tools like Claude Code into daily pipelines, ensuring quality, governance, and scalable data products across analytics and AI initiatives.

You will collaborate with Analytics, Data Science, and Product stakeholders, advancing data platforms while maintaining cost efficiency and reliability.

Qualifications

• 7+ years in data engineering with 3+ years of leadership experience.
• Deep hands-on experience with Snowflake and Apache Airflow in production.
• Strong SQL and Python skills; excellent communication and mentoring ability.

Responsibilities

• Lead, mentor, and grow a data engineering team; manage onboarding and performance.
• Own the technical roadmap and architecture for Enterprise Data platform (Snowflake + Airflow).
• Embed agentic tools into daily workflows for pipeline development and testing.
• Design agent-assisted data quality checks and self-healing pipelines.
• Partner with Analytics, Data Science, Product, and business to deliver data products.
• Establish governance, observability, and cost management across the platform.

Skills

SQL

Python

Leadership

Mentoring

Data governance

Data modeling

Communication

Distributed teams

Education

Bachelor's degree in Computer Science or Engineering

Tools

Snowflake

Apache Airflow

dbt

Fivetran/Airbyte

Snowflake CoCo

Job description

BigCommerce, Inc. is seeking an AI-first Data Engineering Manager to lead Enterprise Data, built on Snowflake and orchestrated with Airflow. You will grow a team, own the roadmap, and embed agentic tools like Claude Code into daily pipelines, ensuring quality, governance, and scalable data products across analytics and AI initiatives.

You will collaborate with Analytics, Data Science, and Product stakeholders, advancing data platforms while maintaining cost efficiency and reliability.