Senior Data Engineer

We are looking for a Senior Data Engineer to help design, build, and scale the data systems that power how Foley operates and grows. In this role, you will build reliable data pipelines, contribute to our enterprise data architecture, and help improve how data is modeled, governed, and accessed across the organization.

This is a hands-on senior engineering role for someone who enjoys solving real technical problems, delivering production-quality data solutions, and improving systems over time. You will partner closely with Engineering, Product, Analytics, and business stakeholders to ensure our data systems are scalable, trusted, ...

The ideal candidate combines strong technical depth with practical execution. They should be comfortable operating in ambiguity, troubleshooting issues as they arise, and delivering well-structured solutions within a defined scope.

Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
Support both batch and event-driven or CDC-based data workflows
Improve pipeline reliability, observability, and performance
Troubleshoot issues in production and proactively solve data quality or system problems
Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
Contribute to data modeling standards and best practices
Implement data governance practices, including role-based access control and trusted data definitions
Support data quality and consistency across systems
Partner with Engineering, Product, Analytics, and business stakeholders to translate requirements into practical data solutions
Communicate clearly about implementation choices, tradeoffs, and technical constraints
Help stakeholders access data that is reliable, understandable, and useful
Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
Embrace new technologies, including AI-enabled tools, where they improve quality or efficiency
Continuously improve system design, maintainability, and team effectiveness
Contribute to the design and evolution of our enterprise data architecture
Help shape scalable patterns for how data is ingested, transformed, stored, and accessed
Apply sound architectural judgment to build solutions that balance scalability, maintainability, and performance
Support a layered data architecture, including bronze, silver, and gold data models

Strong experience building and maintaining ETL/ELT pipelines in production environments
Experience working across both batch and streaming / CDC / event-driven data patterns
Proven ability to design and maintain scalable warehouse schemas and data models within a layered architecture
Strong experience with Python, SQL, and cloud-based data platforms
Experience with AWS data services such as Redshift, Glue, Lambda, S3, and EventBridge
Strong software engineering fundamentals, including reliability, testing, and maintainability
Ability to operate independently and deliver high-quality solutions within a defined technical scope
Strong problem-solving skills and a track record of handling issues in production environments
Experience with medallion architecture and bronze, silver, gold data modeling patterns
Experience with graph data models and entity resolution
Experience implementing data governance practices, including RBAC
Experience supporting machine learning or LLM-related pipelines in production environments
Familiarity with tools or workflows that use AI to improve engineering productivity
Experience partnering with Data Science or Analytics teams to support data products
Enjoy being hands-on and solving concrete technical problems
Think in systems and tradeoffs, not just tools
Can clearly explain what you built, why you built it that way, and what impact it had
Are comfortable owning execution and working through ambiguity
Notice when something is broken or inefficient and take initiative to improve it
Are open to using AI and automation thoughtfully, even if you are not an expert in AI systems
Location: This role is primarily remote, US-based, and operates largely in EST hours, with occasional travel for team collaboration and company events.