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

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).

Staff Data Engineer

Key skills