Senior Data Engineer

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. 
 

What you'll own 

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 governed and hardened data assets. 
   

Business-facing applications and AI-enabled experiences 

• Own the enterprise data platform that enables business teams, analysts, and data scientists to build analytics applications, natural language interfaces, and agent-assisted workflows on trusted data, taking those experiences from proof-of-concept to hardened, governed, and production-ready enterprise capabilities. 

• Build and support custom Python-based and platform-native data applications, including Streamlit-based applications, that enable guided analytics, operational workflows, and business-facing data interactions. 

• Harden and scale business-facing data experiences by ensuring underlying data assets and interfaces are governed, secure, semantically accurate, performant, and built to support sustained enterprise growth. 

• Design data structures, semantic layers, and retrieval patterns that improve accuracy, latency, and response quality for natural language and agent-assisted experiences. 

• Optimize data access patterns, context design, and retrieval logic to manage platform compute, token usage, inference cost, and response quality at scale. 

• 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 governed data assets, feature logic, scoring outputs, and integration patterns, and collaborate with teams using lakehouse or ML platforms for larger-scale workloads. 

Performance, scale, and cost optimization 

• Tune SQL, cloud data platform workloads including Snowflake and Databricks, dbt models, Python components, and data access patterns for performance, scalability, latency, and cost efficiency. 

• Optimize data model design, materialization strategies, incremental processing, and query paths to reduce unnecessary compute, token, and inference cost. 

• Tune data structures, context design, and retrieval patterns to improve response quality, reduce latency, and manage inference cost for natural language and agent-assisted interactions. 

• Evaluate and improve existing data assets and platform capabilities to increase reliability, maintainability, and delivery speed. 

• Support performance improvements for internal data science pipelines, with emphasis on reliable data preparation, efficient transformation logic, and integration back to enterprise workflows. 
  
   

Technical leadership and delivery 

• Lead complex data engineering initiatives from discovery and design through development, hardening, validation, deployment, and adoption. 

• Translate ambiguous business needs into clear technical requirements, data models, business logic, estimates, and delivery plans. 

• Own root cause analysis for data product issues across source data, transformation logic, business rules, pipeline execution, and access controls. 

• Act as a subject matter expert for enterprise data products, SQL, Python, Snowflake, dbt, data modeling, integration patterns, platform hardening, and governed data consumption. 

• Mentor engineers and contractors, and introduce standards, templates, and automation to improve engineering quality and scale delivery across the team. 

• Conduct peer reviews for SQL, Python, dbt models, data models, and technical designs. 

• Evaluate and demonstrate new data platform and AI-enabled capabilities to technical teams and business stakeholders. 

• Work iteratively and collaboratively, with a continuous improvement mindset focused on delivery, quality, and team effectiveness. 

Other responsibilities 

• Uphold F5's Business Code of Ethics and promptly report violations of the Code or other company policies. 

• Perform other related duties as assigned. 
   

Knowledge, skills and abilities 

• Applies AI-assisted development tools and automation with engineering judgment to improve productivity, code quality, and delivery velocity across the team. 

• Ability to translate business processes, enterprise system relationships, and data domain knowledge into scalable, hardened data product design. 

• Advanced SQL development, troubleshooting, query optimization, and data modeling skills. 

• Strong Python development experience for data engineering, automation, APIs, data applications, SaaS integration, or data science pipeline support. 

• Experience hardening and productionalizing data products and pipelines for enterprise-scale governance, reliability, and operational use. 

• Experience with Snowflake, dbt or similar transformation frameworks, and data ingestion and orchestration technologies; familiarity with lakehouse or ML-oriented platforms such as Databricks is valuable for data science workflow support. 

• Experience designing dimensional models, data marts, semantic layers, and reusable data products supporting analytics, custom applications, natural language interfaces, and agent-assisted use cases. 

• Experience integrating structured and semi-structured data from databases, files, APIs, JSON, XML, cloud storage, SaaS platforms, and product telemetry. 

• Understanding of LLM data consumption patterns including context window design, retrieval structure, token and inference cost management, prompt grounding, and data freshness for natural language and agent-assisted experiences. 

• Ability to tune data platform workloads, SQL, and Python components for performance, scalability, latency, compute, token, and inference cost. 

• Understanding of cloud platform concepts across Azure and/or AWS, including storage, compute, identity, security, and managed data services. 

• Ability to lead complex initiatives, mentor others, and influence engineering standards through example and technical guidance. 

• Demonstrates strong analytical, problem-solving, and communication skills, including the ability to explain technical concepts to business, analytics, data science, product, platform, and engineering partners. 
   

• Knowledge of cloud billing datasets and cost optimization is a plus, but not required.
   

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 required. 

• 8 or more years of experience in data engineering, data platform engineering, analytics engineering, or a related technical role, including demonstrated advanced SQL proficiency across query optimization, complex transformation logic, and data modeling. 

• 5 or more years of experience designing data models, data marts, semantic layers, data warehouses, or enterprise data standards. 

• 5 or more years of experience 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 technologies. 

• 3 or more years of Python development experience for data engineering, automation, API integration, custom data applications, SaaS platform integration, or data science pipeline support. 

• Experience hardening and productionalizing data products, pipelines, and platform capabilities for enterprise-scale reliability, governance, and sustained operational use required. 

• Experience with source control and CI/CD practices using tools such as GitLab, GitHub Actions, Azure DevOps, or equivalent required. 

• Experience tuning SQL, data models, transformation workloads, Python components, or cloud data platform usage for performance and cost required. 

Preferred qualifications 

• Demonstrated use of AI-assisted development tools and automation to improve engineering productivity, code quality, and delivery velocity. 

• Deep experience with Snowflake performance tuning, Snowpipe, Snowpark, secure views, data sharing, role-based access controls, and cost optimization. 

• Experience with Streamlit or similar Python-based data application frameworks. 

• Experience with dbt, including macros, tests, documentation, exposures, model governance, semantic layer patterns, and CI/CD integration. 

• Experience with cloud data services across Azure and/or AWS, including storage, identity and access management, managed compute, serverless services, and secure data integration patterns. 

• 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 supporting natural language interfaces, agent-assisted workflows, and AI-enabled analytics experiences, including optimizing data assets and context design for LLM consumption covering token usage, inference cost, prompt grounding, and response latency. 

• Experience with modern orchestration frameworks and deployment automation tools. 

• Experience across multiple enterprise data domains such as customer success, support, sales, finance, subscriptions, installed base, product telemetry, or software fulfillment; experience with product subscription models and telemetry highly desired. 

• Experience in regulated, security-sensitive, or compliance-driven environments.