Senior Data Engineer

About this role

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Skills you'll use

What you'll do

• Design and implement end-to-end automated testing for critical reporting outputs across dbt, LookML, and associated data products.
• Build CI/CD guardrails that prevent unsafe changes from reaching production, including environment separation, release checks, and rollback readiness.
• Define and operate data-quality controls: schema tests, variance monitoring, freshness checks, anomaly detection, and reconciliation to the DMS source-of-truth.
• Establish visibility into dependencies and lineage so teams can assess downstream impact before changing shared fields, models, or contracts.
• Implement agreed standards for V2 Explores, dbt models, semantic definitions, and reusable reporting components.
• Partner with upstream and downstream teams to implement data contracts, schema-change notification, contract tests, and delivery evidence for OEM feeds.
• Participate in and help mature the reporting on-call rotation; lead root-cause analysis, corrective actions, and repeat-incident reduction.
• Document engineering standards and automate their enforcement so quality does not depend on individual memory or manual review.
• Mentor engineers contributing to the reporting stack and review changes against documented specifications and operating standards.
• Implement technical controls that support the agreed data-governance model, including ownership metadata, lineage, quality rules, and controlled change workflows.

Who can apply

• Mentor engineers contributing to the reporting stack and review changes against documented specifications and operating standards.
• Implement technical controls that support the agreed data-governance model, including ownership metadata, lineage, quality rules, and controlled change workflows.
• Five or more years of relevant data-engineering or analytics-engineering experience in production; exceptional four-year candidates may qualify with equivalent end-to-end ownership.
• Advanced SQL and strong data-modeling fundamentals, including explicit grain, dimensional modeling, late-arriving facts, slowly changing dimensions, idempotency, and reconciliation.
• Production experience with dbt or an equivalent transformation framework, including tests, documentation, modular modeling, and CI/CD integration.
• Experience operating a cloud analytical warehouse; BigQuery is preferred, and comparable depth in Snowflake, Redshift, or Synapse is transferable.
• Experience with a semantic or BI modeling layer; LookML/Looker is preferred.
• Proficiency in Python or an equivalent language for data tooling, validation, and automation.

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