Senior Data Engineer

Role overview

Engineering and technical ownership

• Design, build, and optimize pipelines, ingestion frameworks, and platform components for analytics, reporting, and AI/ML.
• Own complex initiatives from design through implementation and rollout; resolve platform performance, scalability, and reliability bottlenecks.
• Improve data models, governance, and access patterns for AI/ML readiness, including feature stores, curated datasets, and streaming access.
• Resolve complex production issues, including on-call support for critical pipeline failures.
• Write well-tested, documented code and infrastructure as code; maintain engineering standards.

Technical leadership and collaboration

• Advise on architecture, design patterns, and best practices; conduct code and design reviews.
• Partner with the Director on design decisions, tool evaluations, and roadmap input.
• Mentor data engineers and collaborate with Product and Engineering stakeholders to turn data needs into technical solutions.
• Document standards, runbooks, and architectural decisions.

Technical qualifications

• Deep data pipeline, optimization, and distributed processing expertise, including performance and scalability troubleshooting.
• Strong knowledge of AWS, Azure, or GCP; modern warehouse/Lakehouse architectures such as Delta Lake or Iceberg.
• Working proficiency in at least two of Python, Scala, and SQL.
• Understanding of governance, security, and compliance in regulated or enterprise environments; ability to troubleshoot and reverse engineer existing pipelines.
• Working knowledge of how data platforms enable AI/ML solutions.

Experience and education

• Bachelor's degree in a related field or demonstrated equivalent experience.
• 7+ years in data engineering, including large-scale mature data platforms.
• Experience owning complex projects through delivery and supporting downstream AI/ML use cases.
• Experience with infrastructure as code and CI/CD, such as Terraform and GitHub Actions; modular code with notebooks.
• Hands-on Snowflake or Databricks and Azure Synapse Analytics or Microsoft Fabric experience.
• Experience mentoring or informally guiding engineers; experience improving developer experience.

Preferred experience

• Agentic workflow design or implementation for data engineering is preferred.
• Property and casualty insurance carrier experience is preferred.
• Data Vault 2.0 or Ensemble data modeling experience is preferred.

Work arrangement

• The posting identifies the role as hybrid.

Benefits

• Competitive salary and professional development and advancement opportunities.
• Medical, dental, vision, and life insurance; short- and long-term disability.
• Company match of 100% of a 6% 401(k) contribution, employee assistance plan, health accounts, and wellness program.