Data Engineer
hirebridge
Job Description
- Build and maintain data pipelines:
- Design, build, and maintain data connectors and ingestion pipelines across Tribute's AWS and GCP environment — including AWS AppFlow, Lambda, EventBridge, S3, Athena, and BigQuery.
- Own pipelines end-to-end: from source connection and ingestion through transformation, modeling, and delivery to downstream consumers in Power BI, Looker, and Looker Studio.
- Build for reliability and maintainability — instrument pipelines appropriately, handle failure gracefully, and leave systems in a state others can work with.
- Identify fragile or missing infrastructure and make a case for what to build next based on business impact.
- Model the data:
- Design data models that reflect how the business actually works — not just what is technically convenient to store.
- Build and maintain LookML models that make data accessible and consistent for analysts and business users.
- Write and optimize BigQuery SQL including scheduled queries, partitioned and clustered tables, and transformation logic that scales.
- Work closely with the eCommerce Operations team and other data consumers to understand how data will be used and model it accordingly.
- Own your work:
- Take a business priority and determine the right technical path to deliver it — without requiring approval on every step.
- Connect your engineering decisions to downstream outcomes: you understand why the data matters and who depends on it.
- Surface technical recommendations proactively — on architecture, tooling, sequencing, or risk — and communicate them clearly to the Director of Data and stakeholders.
- Operate effectively in an environment where requirements are sometimes incomplete and the right answer requires judgment, not just execution.
EDUCATION AND/OR EXPERIENCE:
- Experience:
- 7+ years of hands-on data engineering experience, with meaningful time spent building pipelines and data models in production environments.
- Demonstrated experience working across both AWS and GCP — engineers who have worked deeply in one cloud and have real exposure to the other are a strong fit.
- Track record of building data infrastructure from limited foundations — not inheriting mature systems, but designing and building them.
- Experience working closely with analytics or business teams to translate data needs into engineering solutions.
- Comfort operating with a high degree of autonomy and incomplete specifications.
- Technical Skills:
- AWS — S3, Lambda, EventBridge, Glue/Athena, AppFlow; pipeline design, deployment, and troubleshooting.
- GCP / BigQuery — dataset management, scheduled queries, partitioning and clustering, Standard SQL, performance optimization.
- SQL — advanced, across both Athena and BigQuery dialects; DDL/DML, window functions, complex transformations.
- Python — Lambda authoring, data transformation, scripting and automation.
- LookML — data modeling for Looker; building explores, dimensions, measures, and maintaining model integrity as underlying data evolves.
- GA4 → BigQuery export schema — understanding of the event model and how to work with the raw export.
- Infrastructure-as-code (Terraform or CloudFormation) — strongly preferred.
- dbt or a comparable transformation layer — strongly preferred.
- How you work:
- Business-connected — you understand why the data you build matters and can articulate tradeoffs in terms stakeholders understand.
- Technically opinionated — you have views on the right way to build things and can defend them clearly.
- High ownership; you drive work to completion and flag blockers early rather than waiting.
- Clear communicator with both technical peers and non-technical stakeholders.
- Effective in a lean, fast-moving environment where the right answer often requires initiative, not instructions.