Staff Data Engineer

Foley is a modern SaaS company reimagining how safety-sensitive industries hire and manage risk. They are seeking a Staff Data Engineer to design and scale data systems, lead the development of data pipelines, and establish standards for data governance across the organization.

Responsibilities:

• Lead the design and evolution of Foley’s enterprise data architecture
• Contribute to the ongoing development of our Enterprise Data Model
• Define standards and best practices for data modeling, storage, access, and reliability
• Drive architectural decisions that balance scalability, maintainability, and performance
• Design, build, and maintain scalable data pipelines across internal systems, vendor platforms, and the data warehouse
• Improve data quality, observability, and reliability across core workflows
• Optimize data workflows for efficiency and long-term operational health
• Support both batch and event-driven or CDC-based pipeline patterns
• Own schema design and warehouse modeling strategy, including dimensional and normalized approaches where appropriate
• Help define and implement data governance best practices, including RBAC
• Ensure shared definitions, documentation, and trust in core business data
• Serve as a thought partner to Engineering, Product, Analytics, and business teams
• Translate technical complexity into clear recommendations and tradeoffs
• Raise the bar for data engineering practices across the organization
• Help the company make better decisions through strong, accessible data foundations
• Identify opportunities to improve workflows through automation, tooling, and thoughtful use of AI
• Experiment pragmatically with new technologies and scale what works
• Build systems and guardrails that enable teams to move faster without sacrificing quality

Requirements:

• Strong experience building and maintaining production ETL/ELT pipelines
• Experience working across both batch and streaming / CDC / event-driven data environments
• Proven ability to design scalable warehouse schemas and data models in a layered architecture
• Deep knowledge of database architecture and distributed data systems
• Experience with cross-system entity resolution and complex data modeling
• Experience implementing data governance standards, including RBAC
• Strong software engineering fundamentals, including reliability, backend systems, and data processing
• Ability to operate at a Staff level, setting direction and influencing across teams
• Strong experience with Python, SQL, and AWS data tooling including Redshift, Glue, Lambda, S3 and Data Orchestration (e.g. Airflow)
• Experience supporting ML or LLM-related pipelines in production
• Familiarity with multi-step agent orchestration frameworks such as LangGraph
• Experience partnering with Data Science teams to operationalize data products
• Experience helping organizations introduce AI-enabled workflows in a thoughtful, production-oriented way