Sr Data Engineer - Applied Research & Decision Support

Company

Cox Automotive - USA

Engineering / Product Development

Sr Data Engineer

Individual ContributorHybrid - Ability to work remotely part of the week

Yes, 5% of the time

Day

Compensation

Compensation includes a base salary in the range of $101,500.00 - $169,100.00. 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.

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 (e.g., Claude, Gemini, open-source LLMs) for productivity, prompt engineering, or data enrichment.
• Familiarity with GenAI workflows such as retrieval-augmented generation, prompt engineering, or lightweight fine-tuning, and ability to assess model output quality.
• Experience with automated testing frameworks and data validation techniques to ensure pipeline reliability and data quality.

Preferred Qualifications

• Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Experience building AI agents, intelligent automation, or autonomous data workflows using agent frameworks such as AWS Bedrock AgentCore, Strands, CrewAI, LangGraph, or similar.
• Full-stack software development experience (frontend, backend, networking, APIs) enabling end-to-end ownership of data products and internal tools.
• Experience in the automotive industry or with large-scale marketplace data.

Why Join Our Team

• Build the data infrastructure behind mission-critical products that influence vehicle pricing, fraud prevention, and digital-auction innovation.
• Access modern cloud-native platforms (Snowflake, AWS), GenAI tooling, and rich automotive data sets at scale.
• Collaborate with a diverse group of researchers, engineers, and industry experts in a culture that values curiosity, mentorship, and measurable impact.
• Advanced Analytical Thinking, able to diagnose complex data issues and design scalable solutions that anticipate downstream effects.
• Skilled Business Acumen, understanding how data infrastructure decisions impact analytical products, revenue, and customer experience.
• Skilled Communication, able to convey technical architecture decisions and trade-offs clearly to both technical and non-technical audiences.
• Proficiency with visualization tools such as Tableau, Streamlit, or similar, for insight communication and stakeholder reporting.

Travel: 0-10%

Hybrid: ability to work in-office 2-3 days per week.

To be employed in this role, you’ll need to clear a pre-employment drug test. Cox Automotive does not currently administer a pre-employment drug test for marijuana for this position. However, we are a drug-free workplace, so the possession, use or being under the influence of drugs illegal under federal or state law during work hours, on company property and/or in company vehicles is p