Sr Data Engineer

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

• Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship. No OPT, CPT, STEM/OPT or visa sponsorship now or in future.
• Must be located in Atlanta GA or be willing to relocate at own expense
• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience

• Must have a minimum of 4 years experience in Data Engineering, building production ETL/ELT pipelines, with strong Python and advanced SQL

• Snowflake depth: schema and view design, MERGE and history-table patterns, bulk loading and external stages, role-based access, masking policies, warehouse sizing, and query profiling

• REQUIRED: strong experience with AWS data services in production: serverless compute, object storage, workflow orchestration, database migration/CDC, NoSQL, query and catalog services, event scheduling, secrets management, and IAM

• Infrastructure-as-code and release automation: CloudFormation or Terraform, Git branching strategies, and CI/CD pipelines with automated deployment of database objects

• Ingesting from heterogeneous sources: relational databases and change data capture, ERP and CRM systems, SaaS APIs, and flat files

• Data modeling for analytics: dimensional and denormalized models, conformed layers, and the difference between raw, hub, and mart responsibilities

• Data quality and observability: validation frameworks, reconciliation, SLA and freshness monitoring, alerting, and root-cause analysis on late or partial loads

• Data security and governance: PII identification and masking, least-privilege access, and metadata and lineage practices

• Ability to work in an Agile Release Train with an Agile tracking tool, write clear user stories and acceptance criteria, and communicate status and risk to Product and Engineering leadership

• Experience mentoring engineers and reviewing code; comfortable leading work across onshore and offshore teams

• Hands-on experience with AI-assisted engineering tools and spec-driven development, including building reusable skills or agents for data tasks

• Familiarity with Snowflake AI features, dynamic tables, and Model Context Protocol (MCP) access patterns for exposing governed data to AI agents

• dbt, Apache Airflow or similar orchestration and integration platforms

• Experience with data catalog and governance tooling

• BI semantic-layer experience and an understanding of how data models feed self-service analytics

• Domain knowledge in automotive, finance, sales, marketing, or contact-center data

• AWS certifications (Solutions Architect, Data Engineer) or SnowPro certification

• Experience in large-scale platform migrations and legacy-code modernization