Data Engineer

Safe-Guard Products International•Atlanta, GA

1d•Hybrid

About The Position

The Data Engineer leads complex data engineering projects and drives innovation in data architecture, automation, and AI-enabled data products. The role builds pipelines and data products used by OEM partners, dealers, and internal teams; uses AI tools in daily engineering work; and contributes to decisions about which data products to build. It is described as a technical product owner role, with a path toward full data product ownership for the right person. The role oversees end-to-end delivery of large-scale data engineering initiatives, including pipeline quality, SLAs, and adoption metrics. It prototypes AI-enabled data products and advances promising ideas from proof of concept to measured pilots. The Data Engineer works with business and client stakeholders to frame problems, define value hypotheses, and recommend what to build, while collaborating with architects on scalable and secure data architectures. The role also automates data ingestion, transformation, testing, and deployment; monitors pipelines and resolves issues at their root cause; and guides junior engineers through code reviews and knowledge sharing. As readiness is demonstrated, the position can grow to include roadmap and backlog decisions, stakeholder discovery, adoption tracking, and domain governance.

Requirements

Bachelor's degree in information systems, Computer Science, or a related field; an equivalent combination of education and experience will be considered.
Four or more years of experience in data engineering, or equivalent demonstrated impact.
Proficiency in SQL and Python.
Experience with data pipeline and transformation tools such as Apache Airflow, dbt, Snowflake, or AWS Glue.
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Working familiarity with at least one analytical or semantic-layer tool, such as Power BI, Looker, or 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 technical and business partners, and the ability to lead projects and mentor others.

Nice To Haves

Hands-on experience building and shipping something with AI or LLMs, such as an agent, a retrieval workflow, or an AI-assisted engineering practice, that can be demonstrated.
A record of starting something nobody asked for, such as an internal tool, an open-source project, a process, or a venture.
Curiosity about the automotive finance and insurance business and how data products create value for clients.

Responsibilities

Oversee end-to-end delivery of large-scale data engineering initiatives, owning pipeline quality, SLAs, and adoption metrics for shipped data products.
Use AI coding assistants and agents in daily engineering work and share effective prompts, skills, and workflows with the team.
Prototype AI-enabled data products, such as natural-language access, semantic layers, and agent workflows, and advance promising ideas from proof of concept to measured pilot.
Meet with business and client stakeholders to frame problems, define value hypotheses, and recommend what to build or not 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 their root cause.
Guide and develop junior engineers through code reviews and knowledge sharing.
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 readiness is demonstrated.