Senior Data Engineer

At F5, we strive to bring a better digital world to life. Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world. We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation.    Everything we do centers around people. That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive. The   Sr. Data Engineer – Data Platform & Engineering   designs, builds, hardens, and   productionalizes   enterprise data products, transformation logic, curated data layers, and platform integrations across F5's data ecosystem. This is a hands-on engineering role requiring daily development across SQL, Python, Snowflake, and   dbt , with a focus on shifting from building data assets to deploying, governing, and productizing them at scale.     The role works across F5's enterprise data platform, Python-based data applications, cloud data services, SaaS platforms, and adjacent data platforms used by product or data science teams. The Sr. Data Engineer partners with cross-functional business and technical partners to deliver trusted insights through governed, secure, and scalable data assets and platform capabilities that support reporting, analytics, operational workflows, AI-enabled business experiences, and internal ML/data science use cases.   Attractions of the job   Data Platform & Engineering sits at the center of how F5 turns data into decisions and experiences. This team owns   the   enterprise data platform that enables business teams, analysts, data scientists, and product teams to build analytics applications, natural language interfaces, and agent-assisted workflows.   The hardest and most technically demanding part of that work belongs here. Anyone can   get to   70%. This team owns the last 30%, building the governed, secure, and scalable platform and interfaces that deliver trusted insights at enterprise scale, and make the difference between a promising prototype and a capability the business can trust, build on, and grow with.

Data platform and engineering   Design, develop, and ship enterprise data products,   dbt   transformation logic, and Python-based data workflows that deliver trusted insights across analytics, reporting, business-facing applications, natural language interfaces, agent-assisted workflows, and internal data science pipelines.   Develop SQL and Python code for data transformation, business logic, automation, API integration, and SaaS platform integration.   Build and   optimize   Snowflake and   dbt   assets, including tables, views, transformation models, stored procedures, tests, macros, and governed access patterns.   Design dimensional, logical, and semantic data models, implementing business rules, standard metrics, validation logic, and reusable data definitions across enterprise data domains.   Engineer data assets and access patterns with LLM and inference consumption in mind, including context window design, retrieval structure, prompt grounding, and data freshness requirements for agent-assisted and natural language experiences.   Harden data products and platform capabilities through governed access patterns, security controls, audit fields, and operational reliability standards.   Develop integration logic using APIs, connectors, cloud services, and SaaS platform capabilities to bring together data from enterprise systems, product telemetry, files, JSON, XML, and cloud storage.   Apply CI/CD practices and build reusable engineering patterns, standards, and templates for SQL, Python,   dbt , data modeling, and integration logic to scale delivery across the team.   Identify   and drive opportunities to improve data trust, reduce manual reconciliation, and increase reuse of