The Decision Support organization provides data-driven insights, advanced analytics, and scalable data products to inform operational, strategic, and product-related decisions across Cox Automotive. Within Decision Support, the Applied Research team serves as the innovation engine, developing and operationalizing cutting-edge solutions across vehicle valuation, fraud detection, market research, and AI-driven decisioning-such as vehicle information enhancement, fraud detection, and machine learning for digital auction solutions.
As a Senior Data Engineer on the Applied Research team, you design, build, and maintain the data infrastructure and pipelines that power the team's analytical products and models. You partner with data scientists, business intelligence analysts, and stakeholders across Decision Support to translate analytical requirements into scalable, reliable data architectures using Snowflake, AWS, and modern orchestration tools. You ensure data is accessible, trusted, and readily consumable, while driving automation, building strong semantic and context layers that enable AI and self-service analytics, reducing technical debt, and establishing reusable frameworks that extend value across Decision Support
WHAT YOU'LL DO
Data Architecture and Pipeline Engineering
- Design and implement robust, scalable data pipeline architectures using Snowflake, AWS (S3, Lambda, EC2), and modern orchestration tools to support analytical models, data products, and reporting across Decision Support.
- Build and maintain optimal ETL/ELT workflows for structured and unstructured data, ensuring alignment with enterprise architecture standards and business requirements.
Data Quality and Reliability
- Develop and execute automated testing and validation frameworks to ensure data integrity, pipeline reliability, and system stability across all analytical outputs.
- Monitor and troubleshoot data anomalies, proactively identifying root causes and implementing fixes to maintain high standards of data quality.
Platform and Infrastructure Development
- Operationalize data science models by building the infrastructure required for deployment, monitoring, and refresh schedules in cloud environments.
- Automate manual data processes, transforming them into repeatable, scalable capabilities that reduce technical debt and free data scientist capacity for higher-value work.
- Develop tools and programming to cleanse, organize, and transform data leveraging AI, ML, and big data techniques. Design and maintain semantic layers, context layers, and metadata structures that enable AI-powered workflows, GenAI applications, and self-service data access across the organization.
- Design, build, and maintain AI agents and intelligent automation workflows that streamline data operations, accelerate insight delivery, and extend the team's capacity across Decision Support.
Collaboration and Stakeholder Engagement
- Partner with data scientists, business intelligence analysts, product owners, and the broader Decision Support team to translate analytical requirements into logical and physical database designs.
- Collaborate with internal and external data providers on data validation, providing feedback and making customized changes to data feeds and mappings for analytical and operational use.
Process Improvement and Innovation
- Identify and implement improvements to internal data management processes, influencing the data infrastructure roadmap through technical leadership and innovation.
- Mentor junior data scientists, engineers, and analysts, contribute to design standards and assurance processes, and establish reusable data frameworks that extend value across Decision Support.
WHO YOU ARE
Minimum Qualifications
- Qualified candidates will live within a commutable distance to the Atlanta office and work in a hybrid model
- 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.
- Bachelor's degree in a related field with 4+ years of experience, or an equivalent combination of education and experience (e.g., Master's degree and 2 years of experience, Ph.D. and up to 1 year of experience, or 16 years of experience in a related field).
- Strong Python programming with libraries such as Pandas, PySpark, and SQL proficiency, with experience building and optimizing complex queries, data transformations, and pipeline logic.
- Proven experience designing and building data pipelines and architectures in cloud environments, including hands-on use of Snowflake and AWS services such as S3, Lambda, and EC2.
- Experience with ETL/ELT processes, data modeling, data warehousing concepts, and big data technologies (e.g., Spark, Kafka).
- Familiarity with data orchestration tools such as Apache Airflow, dbt, or Dagster, and experience with CI/CD pipelines for data engineering.
- Hands-on experience with GenAI tools