Bachelor's degree in information systems, Computer Science, or related field; equivalent experience considered.
4+ years of experience in data engineering or demonstrated impact.
Proficiency in SQL and Python.
Experience with data pipeline and transformation tools (e.g., Apache Airflow, dbt, Snowflake, AWS Glue).
Familiarity with cloud platforms (AWS, Azure, GCP).
Strong data modeling and data warehousing skills.
Experience with CI/CD and DevOps practices.
Responsibilities
Oversee end-to-end delivery of large-scale data engineering initiatives, ensuring pipeline quality and adoption metrics.
Utilize AI coding assistants in daily work and share effective workflows with the team.
Prototype AI-enabled data products and transition successful concepts to pilot phases.
Engage with stakeholders to define problems and recommend data product developments.
Design scalable and secure data architectures in collaboration with architects.
Implement automation for data ingestion, transformation, testing, and deployment.
Monitor data pipelines proactively and resolve technical issues at their root.
Benefits
Medical, Dental, and Vision Insurance
Flexible Spending Account
Health Savings Account
401(k) Plan with Company Match
Company-paid Short-Term and Long-Term Disability
Company-paid Life Insurance
Paid Holidays and Vacation
Employee Referral Program
Employee Assistance Program
Wellness Programs
Paid Community Service Opportunities
Tuition Reimbursement
Ongoing Training & Personal Development
And More!
Full Job Description
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Job Title: Data Engineer (hybrid)
Location: US-GA-Atlanta (Sandy Springs)
FLSA: Exempt
#LI-Hybrid
Job Overview:
The Data Engineer leads complex data engineering projects and drives innovation in data architecture, automation and AI-enabled data products. This role builds the pipelines and data products our OEM partners, dealers and internal teams rely on, works with AI tools every day, and helps decide which data products are worth building. It is a technical product owner role today, with a path to full data product ownership for the right person.
Job Responsibilities:
Oversee the end-to-end delivery of large-scale data engineering initiatives, owning pipeline quality, SLAs and adoption metrics for the data products you ship.
Use AI coding assistants and agents in daily engineering work, and share prompts, skills and workflows that make the whole team faster.
Prototype AI-enabled data products, such as natural-language access, semantic layers and agent workflows, and take the best ideas from proof of concept to a measured pilot.
Meet with business and client stakeholders to frame problems, define value hypotheses and recommend what to build and what not to build.
Design scalable and secure data architectures in collaboration with architects.
Implement automation for data ingestion, transformation, testing and deployment.
Proactively monitor data pipelines and resolve technical issues at the root cause.
Guide and develop junior engineers through code reviews and knowledge sharing and bring the team at least one new idea each quarter backed by a working prototype.
Grow toward data product ownership by taking on roadmap and backlog decisions, stakeholder discovery, adoption tracking and domain governance as you demonstrate readiness.
The above statements are intended only to describe the general nature of the job and should not be construed as an all-inclusive list of position responsibilities.
Job Requirements:
Bachelor's degree in information systems, Computer Science, or related field; equivalent combination of education and experience will be considered.
4 or more years of experience in data engineering, or equivalent demonstrated impact.
Proficiency in SQL and Python.
Experience with data pipeline and transformation tools (e.g., Apache Airflow, dbt, Snowflake, AWS Glue).
Familiarity with cloud platforms (AWS, Azure, GCP).
Working familiarity with at least one analytical or semantic-layer tool (e.g., Power BI, Looker, Tableau).
Strong data modeling and data warehousing skills.
Working knowledge of distributed data processing.
Experience with CI/CD and DevOps practices.
Strong communication skills with both technical and business partners, and the ability to lead projects and mentor others.
Preferred: hands-on experience building and shipping something with AI or LLMs (for example an agent, a retrieval workflow or an AI-assisted engineering practice) that you can demonstrate.
Preferred: a record of starting something nobody asked for, such as an internal tool, an open-source project, a process or a venture.
Preferred: curiosity about the automotive finance and insurance business and how data products create value for clients.
