Data Engineer

We're so happy you're here! Thank you for checking our job out and we hope to have the chance to meet you in our interview process!

About the role

Data sits behind nearly every decision at Grüns . As we scale, our reporting, operations, internal tools, and AI initiatives all depend on pipelines that are fast, accurate, and trusted. This role builds and owns that infrastructure. You'll take a business problem, design the pipeline behind it, and stay with it through production.

This role is part of our remote HQ! We have a fully remote, high-trust work environment - and also come together on a biannual basis for amazing off-sites where we can connect IRL.
In this role, you will:

- Design, build, and own custom data pipelines from source systems into our warehouse, including integrations with messy or poorly documented APIs.

- Build and operate Airflow DAGs for scheduled and event-triggered workflows.

- Develop dbt models and a governed semantic layer that analysts, internal tools, and AI agents can trust.

- Develop reusable patterns for incremental syncs, retries, replay, backfills, and event processing so the team ships faster over time

- Build monitoring, quality checks, and reconciliation that catch problems before bad data reaches reports, teams, or AI tools

- Diagnose and resolve production incidents involving missing, delayed, duplicated, or incorrect data

- Partner with Finance, Operations, Growth, and Technology to turn ambiguous requests into durable data products

- Use AI coding agents to accelerate implementation, testing, and debugging while remaining accountable for architecture, correctness, and quality.

- Document architectural decisions, system ownership, and recovery procedures so critical knowledge is accessible and systems remain supportable.

We're looking for someone who:

- Has 3+ years in data engineering, analytics engineering, or a closely related engineering role, with a track record beyond execution-only work

- Writes production-quality Python and advanced SQL, with an emphasis on maintainable, well-tested code.

- Has designed, built, and operated Airflow workflows for custom data pipelines.

- Has strong dbt and dimensional modeling fundamentals: grain, keys, facts, dimensions, incremental models, tests, documentation, and schema evolution

- Has built custom integrations against third-party APIs, webhooks, files, or event streams, not just configured managed connectors

- Knows API ingestion deeply: authentication, pagination, rate limits, incremental synchronization, retries, and historical backfills

- Has owned at least one business-critical pipeline from design through production support, including monitoring, alerting, logging, reconciliation, CI/CD, and runbooks

- Has worked in a high-growth environment, ideally DTC or subscription, where priorities and source systems change often

- Brings broad, hands-on experience across the data stack rather than specializing in one narrow area.

- Uses AI coding agents fluently for implementation, testing, debugging, refactoring, and review, with the judgment to catch what they get wrong

- Experience with BigQuery, GCP, and Cloud Composer is a plus

Approach to the role:

- Takes ownership from problem definition through production operation, including the unglamorous monitoring, debugging, and maintenance

- Starts with the business decision and the source of truth before choosing a technical solution

- Thinks in failure modes: what breaks, who it affects downstream, and how it recovers

- Moves fast and independently without normalizing fragile shortcuts or one-off scripts

- Digs into discrepancies until the real cause surfaces, not the first plausible explanation

- Speaks up clearly when systems, definitions, or stakeholder expectations conflict

- Documents decisions and builds patterns that make everyone else faster

- Uses AI coding agents to accelerate implementation, testing, debugging, and review while maintaining strong