Data Engineer

Department: Infrastructure & Operations

Location: Atlanta HQ (preferred); Remote US or Remote UK considered

Reports to: Engineering Manager, Infrastructure & Operations

Are you a builder who loves to look under the hood? As a Data Engineer on our Infrastructure & Operations team, you'll own the pipelines and transformations that turn raw data from across our business — e-commerce, logistics, cloud services, and more — into the clean, reliable datasets that power decision-making company-wide. The data behind every pedal stroke, heart rate reading, and training session tells a story. This is a high-ownership role on a small, collaborative team where you'll be our go-to person for ELT, SQL, and getting the right data to the right people at the right time. If you’re curious, collaborative, and ready to take full ownership of a modern data stack, your next big build starts here.

What You’ll Do

• Systems Understanding & Documentation: Inherit and thoroughly document our existing data infrastructure — pipelines, transformations, dependencies, and operational patterns. Building a clear picture of what exists and why is the first priority.
• ELT Pipeline Ownership: Maintain and evolve pipelines that move data from source systems — including NetSuite, Parse/MongoDB, Magento, and third-party APIs — through transformation and into our Redshift data warehouse.
• Data Modeling & SQL: Write and optimize SQL to build views, materialized views, and datasets in Redshift that serve the BI team and stakeholders across the company.
• Tooling & Reliability: Maintain our data stack (dbt Cloud, Airflow, Airbyte, Rivery/Boomi), ensuring pipelines are reliable, observable, and well-documented.
• BI Partnership: Partner closely with the BI team (Metabase, Power BI) to understand data needs and deliver clean, well-modeled datasets they can trust.
• Pipeline Health: Monitor pipeline health, troubleshoot data quality issues, and build checks and alerting that prevent problems from reaching downstream consumers.
• Infrastructure Support: Support and improve our data infrastructure running on AWS (Lambda, S3, Redshift, EC2, DMS).
• Knowledge Sharing: Share expertise in ELT patterns, data modeling, and tooling with the wider Infrastructure & Operations team, building data literacy across the group.
• Cross-functional Contribution: Pitch in on broader infrastructure and operations work as you grow — small teams value engineers who are curious beyond their core specialty.

What We’re Looking For

• 3–5 years of experience in data engineering, analytics engineering, or a similar role — including experience inheriting, stabilizing, or documenting existing systems you didn't build
• Strong SQL skills — complex queries, performant views, and data modeling in a warehouse environment
• Experience with at least one of: dbt, Airflow, Airbyte, or similar ELT/orchestration tools
• Familiarity with cloud data warehouses (Redshift, BigQuery, Snowflake, or similar)
• Working knowledge of Python or another scripting language for data tasks
• Strong debugging instincts — comfortable diagnosing issues in unfamiliar pipelines and codebases without the original author available
• Exposure to BI tools (Metabase, Power BI, Looker, or similar) and how analysts consume data
• Good communication skills and comfort explaining data concepts to non-technical stakeholders
• Comfort operating with significant ownership and autonomy, including during an ambiguous ramp period

Nice to Have:

• Experience with AWS services, particularly Lambda, S3, and EC2
• Git-based workflows and version-controlled analytics (e.g., dbt projects in GitHub)
• Familiarity with CI/CD pipelines and modern development practices
• Interest in fitness, endurance sports, or connected hardware
• Experience using AI coding tools (e.g., Claude Code, GitHub Copilot, OpenAI Codex, Cursor, or similar) to accelerate development and data work

Success Indicators

• Within 90 days: Existing pipelines, dependencies, and operational patterns are documented; the candidate has a clear and confident picture of system state and can diagnose issues independently
• Pipelines are reliable, observable, and well-documented — the BI team trusts the data they receive
• Data quality issues are caught before reaching downstream consumers, with alerting in place
• Stakeholders across Sales, Finance, and Product can self-serve on clean datasets without ad hoc engineering requests
• Teammates across Infrastructure & Operations have meaningfully improved their ELT literacy through knowledge sharing
• The role has grown beyond pure data engineering, contributing to broader infrastructure and operations work