Sr Data Engineer

Compensation includes a base salary in the range of $ - $. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate’s knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.

Cox Automotive’s Enterprise Platforms Data & Analytics organization owns the pipelines, Snowflake data layers, and governed data products that feed Finance, Sales, Marketing, Service, and the company’s growing portfolio of AI initiatives. Data lands in Snowflake from dozens of enterprise sources through a metadata-driven AWS framework, is modeled into conformed and mart layers, and is exposed to BI tools, downstream applications, and AI agents under strict governance and PII controls.

This engineer is a hands‑on technical leader on one of the Data & Analytics delivery pods. You will build and ship production pipelines and governed views, raise the quality bar through validation and peer review, help production support teams resolve complex data incidents, and mentor a mixed onshore and offshore team. The work sits at the point where data engineering meets AI: the datasets you deliver are the foundation for BI today and for AI-assisted analytics tomorrow, and you will use AI-assisted development responsibly to deliver them faster.

• Design, build, and operate metadata-driven ingestion and egress pipelines that land data from enterprise applications, databases, and third‑party APIs into Snowflake, using a shared, configuration-driven AWS framework (serverless compute, orchestration, change data capture, object storage, metadata registries, and infrastructure-as-code)
• Own end‑to‑end delivery of features from refined requirement to production: source onboarding, raw-to-hub modeling, SQL transformations, validation, and release through a controlled multi‑environment CI/CD flow
• Build governed consumption layers in Snowflake: conformed and base views, business‑specific models, marts, and role‑based access, with PII masking and data‑quality verification shipped alongside every view
• Extend and harden shared libraries and frameworks (SLA monitoring, asset registration, alerting, connection management, and code generation) so new sources onboard through configuration rather than custom code
• Define and enforce data‑quality, reconciliation, and peer‑validation standards, including row‑count, orphan, duplicate, and freshness checks that gate every promotion to production
• Lead production readiness and support escalations alongside production support teams: diagnose stale or partially loaded tables, replication and ingestion failures, role and permission regressions, authentication changes, and month‑end reporting issues
• Drive the migration of legacy code into domain‑owned repositories with clear ownership, CI/CD, and catalog metadata
• Use AI‑assisted development (coding assistants, spec‑driven development, reusable skills) to raise throughput without lowering the review and testing bar, and help formalize guardrails for the team
• Partner with Product Owners, architects, and consuming business and AI teams to translate business questions into reliable, documented, governed datasets that serve both BI and AI workloads
• Mentor junior and offshore engineers, review pull requests against architecture and design standards, and contribute to shared standards for ingestion, modeling, governance, and cataloging
• Produce documentation, runbooks, and operational playbooks, and keep work items, features, and dependencies accurate for the Release Train

Required Skills Applicants must currently be authorized to work in the United States for…