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Lead Data Engineer (Hybrid)American Family Insurance • El Paso, Texas, United States
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Lead Data Engineer (Hybrid)

American Family Insurance
  • US
    El Paso, Texas, United States
  • US
    El Paso, Texas, United States
Jetzt Bewerben

Über

Overview American Family Insurance is looking for an experienced and driven Lead Data Engineer to guide our Data Platform team. In this important role, you will oversee a team of engineers advancing our service‑oriented data and AI platform. The Data and AI Platform team influences multiple departments, including security and AI, at AmFam. You will create tools and infrastructure that empower other teams and departments within American Family Insurance. You will report to a Senior Manager, Data Engineering. This flex office/home position requires you to work a minimum of 10 days per month from the Madison, WI 53783 office. Key Responsibilities Partner with stakeholders to refine undefined use cases into clear requirements, translate those into technical specs, and implement working solutions. Design and build platform capabilities that multiple teams consume, including feature stores, observability tooling, evaluation pipelines, and deployment patterns. Define and implement reusable patterns and infrastructure: shared LLM serving, evaluation frameworks, CI/CD templates, internal SDKs. Assess existing AI applications and establish standards for code quality, testing, deployment, and observability. Mentor data scientists and data engineers, conduct meaningful code reviews, and provide architectural guidance across the platform. Facilitate communication across project teams, architects, and business partners. Document critical workflows, standards, and operational support aspects of the platform. Required Experience and Skills Demonstrated experience building AI/ML systems that run in production at scale - not notebooks or prototypes, but real software serving real users. Proven ability to take ambiguous, undefined problems and deliver shipped products. Strong system design skills with an emphasis on scalability, failure modes, and long‑term maintainability. Deep software engineering fundamentals: experience with large codebases, design patterns, testability, and enterprise SDLC. Hands‑on experience with LLMs in production: RAG, agents, evaluation, prompt engineering, or fine‑tuning. Expert‑level Python programming in a software engineering context, not just scripting or pipelines. Experience designing and building APIs for internal or external consumption. Deep experience with GCP, specifically Vertex AI, Pub/Sub, BigQuery, and NoSQL databases (e.g., Firestore, Bigtable). Applied knowledge of Kubernetes for container orchestration and workload management. Experience with infrastructure‑as‑code tools (e.g., Terraform, Docker) and CI/CD pipelines. Travel and Relocation Up to 10% travel required. Relocation support is offered for eligible candidates. Compensation Salary range: $131,000 – $220,000. Compensation may vary based on the job level and your geographic work location. Benefits Comprehensive medical, dental, vision, and wellbeing benefits. Competitive 401(k) contribution, pension plan, annual incentive, 9 paid holidays, and a paid time off program (23 days accrued annually for full‑time employees). Student loan repayment program and paid‑family leave. EEO Statement We are an equal opportunity employer. It is our policy to comply with all applicable federal, state and local laws pertaining to non‑discrimination, non‑harassment and equal opportunity. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. American Family Insurance is committed to the full inclusion of all qualified individuals. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please email AskHR@AmFam.com to request a reasonable accommodation.
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  • El Paso, Texas, United States

Sprachkenntnisse

  • English
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