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, 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. You will partner with cross-functional business and technical teams to deliver trusted insights through governed, secure, and scalable data assets that support reporting, analytics, operational workflows, AI-enabled experiences, and internal ML/data science use cases.
This role will also be re-imagining legacy business intelligence platforms to be driven by AI / agents and is a great opportunity for anyone looking to be more hands on with applying AI, beyond prototypes and solving the challenges of implementing AI-powered data assets in practical business settings.
Attractions of the job
Demonstrating an AI use case is one thing, but deploying them in business settings is a different ball game. The hardest and most technically demanding data work at F5 belongs to this team. Anyone can get to 70%. This team owns the last 30%: building the platform and interfaces that make the difference between a promising prototype and a capability the business can trust, build on, and grow with. Everything you ship meets a baseline standard: governed, secure, hardened, and production-ready.
What you'll own
Data platform and engineering
- Design, develop, and ship enterprise data products, dbt transformation logic, and Python-based data workflows, covering transformation, business logic, automation, and API/SaaS integration.
- Build and optimize Snowflake and dbt assets, including tables, views, transformation models, stored procedures, tests, macros, and access patterns.
- Design dimensional, logical, and semantic data models, implementing business rules, standard metrics, validation logic, and reusable data definitions across enterprise domains.
- Develop integration logic using APIs, connectors, cloud services, and SaaS platforms 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 to scale delivery across the team.
- Optimize model design, materialization, incremental processing, and query paths across Snowflake, Databricks, and Python to reduce compute, latency, and cost.
Business-facing applications and AI-enabled experiences
- Build and support custom Python-based and platform-native data applications, including Streamlit-based apps, for guided analytics, operational workflows, and business-facing data interactions.
- Engineer data assets, semantic layers, and retrieval patterns for LLM and inference consumption (context window design, retrieval structure, prompt grounding, token/inference cost, and data freshness) to improve accuracy and latency for natural language and agent-assisted experiences.
- Partner with analytics, data science, product, and business teams to translate decision workflows into reusable data products and application-ready datasets.
- Support internal data science and ML workflows by preparing data assets, feature logic, scoring outputs, and integration patterns.
Technical leadership and delivery
- Lead complex data engineering initiatives from discovery and design through deployment and adoption, translating ambiguous business needs into clear requirements, models, and delivery plans.
- Act as a subject matter expert for enterprise data products, SQL, Python, Snowflake, dbt, data modeling, integration patterns, and governed data consumption.
- Mentor engineers and contractors, conduct peer reviews, and introduce standards, templates, and automation to improve quality and scale delivery.
Qualifications
- Demonstrated experience designing and building enterprise data platform capabilities at scale, with hands-on development as the primary mode of work.
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Mathematics, Information Systems, or equivalent combination of education and experience.
- 8+ years in data engineering, data platform engineering, analytics engineering, or a related technical role, including advanced SQL proficiency across query optimization, complex transformation logic, and data modeling.
- 5+ years designing data models, data marts, semantic layers, data warehouses, or enterprise data standards.
- 5+ years developing ETL/ELT, transformation logic, curated data products, or application-ready data layers using tools such as Snowflake, dbt, Azure Data Factory, Fivetran, or equivalent.
- 3+ years of Python development for data engineering, automation, API integration, custom data applications, SaaS integration, or data science pipeline support.
- Experience hardening and productionalizing data products, pipelines, and platform capabilities for enterprise-scale reliability and sustained operational use.
- Experience with source control and CI/CD using tools such as GitLab, GitHub Actions, Azure DevOps, or equivalent.
- Experience tuning SQL, data models, transformation workloads, Python components, or cloud data platform usage for performance and cost.
Preferred qualifications
- Deep experience with Snowflake performance tuning, Snowpipe, Snowpark, secure views, data sharing, role-based access controls, and cost optimization.
- Experience with dbt (macros, tests, documentation, exposures, model governance, semantic layer patterns, CI/CD) and Streamlit or similar Python-based data application frameworks.
- Experience supporting natural language interfaces, agent-assisted workflows, and AI-enabled analytics, including optimizing data assets and context design for LLM consumption (token usage, inference cost, prompt grounding, response latency).
- Experience with Databricks, Spark, Delta Lake, or MLflow in partnership with data science or product teams, including feature generation, model input datasets, scoring outputs, and workflow integration.
- Experience across multiple enterprise data domains such as customer success, support, sales, finance, subscriptions, installed base, product telemetry, or software fulfillment; product subscription models and telemetry highly desired.
Other responsibilities
- Uphold F5's Business Code of Ethics and promptly report violations of the Code or other company policies.
#LI-KT1 #Hybrid
The Job Description is intended to be a general representation of the responsibilities and requirements of the job. However, the description may not be all-inclusive, and responsibilities and requirements are subject to change.
Please note that F5 only contacts candidates through F5 email address (ending with @f5.com) or auto email notification from Workday (ending with f5.com or @myworkday.com).
Equal Employment Opportunity
It is the policy of F5 to provide equal employment opportunities to all employees and employment applicants without regard to unlawful considerations of race, religion, color, national origin, sex, sexual orientation, gender identity or expression, age, sensory, physical, or mental disability, marital status, veteran or military status, genetic information, or any other classification protected by applicable local, state, or federal laws. This policy applies to all aspects of employment, including, but not limited to, hiring, job assignment, compensation, promotion, benefits, training, discipline, and termination. F5 offers a variety of reasonable accommodations for candidates. Requesting an accommodation is completely voluntary. F5 will assess the need for accommodations in the application process separately from those that may be needed to perform the job. Request by contacting [email protected].
Skills Required
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Mathematics, Information Systems, or equivalent
- 8 or more years of experience in data engineering, data platform engineering, analytics engineering, or related technical role
- Advanced SQL development, troubleshooting, query optimization, and data modeling skills
- 5 or more years designing data models, data marts, semantic layers, data warehouses, or enterprise data standards
- 5 or more years developing ETL/ELT, transformation logic, curated data products, or application-ready data layers using Snowflake, dbt, Azure Data Factory, Fivetran, or equivalent
- 3 or more years of Python development experience for data engineering, automation, API integration, or data applications
- Experience hardening and productionalizing data products, pipelines, and platform capabilities for enterprise-scale reliability and governance
- Experience with source control and CI/CD practices using tools such as GitLab, GitHub Actions, or Azure DevOps
- Experience tuning SQL, data models, transformation workloads, Python components, or cloud data platform usage for performance and cost
- Experience integrating structured and semi-structured data from databases, files, APIs, JSON, XML, cloud storage, SaaS platforms, and telemetry
- Understanding of cloud platform concepts across Azure and/or AWS, including storage, compute, identity, security, and managed data services
F5 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about F5 and has not been reviewed or approved by F5.
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Equity Value & Accessibility — Equity grants and an employee stock purchase plan are positioned as meaningful parts of total compensation, with RSUs and a discount ESPP commonly included. Pay packages for many technical roles are considered competitive when equity is taken into account.
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Leave & Time Off Breadth — Paid vacation that increases with tenure, sick time, paid holidays, and paid family leave are prominently featured. Additional programs like volunteer time and periodic wellness long weekends are highlighted as part of the time-off ecosystem.
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Inclusive Benefits Coverage — Health plans include travel support for specific care (such as reproductive and gender‑affirming services) and mental health resources, alongside comprehensive medical, dental, and vision coverage. These elements are presented as part of a broad, inclusive approach to healthcare.
F5 Insights
What We Do
F5 application services ensure that applications are always secure and perform the way they should—in any environment and on any device. F5 (NASDAQ: FFIV) powers applications from development through their entire life cycle, across any multi-cloud environment, so our customers – enterprise businesses, service providers, governments, and consumer brands—can deliver differentiated, high-performing, and secure digital experiences.
