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 ownstheenterprise 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 canget to70%. 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.

Whatyoullown

Data platform and engineering

• Design develop and ship enterprise data productsdbttransformation 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 andoptimizeSnowflake anddbtassets 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 Pythondbt data modeling and integration logic to scale delivery across the team.

• Identifyand 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 includingStreamlit-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 semanticallyaccurate 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.

• Optimizedata 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 andcollaboratewith teams usinglakehouseor ML platforms for larger-scale workloads.

Performance scale and cost optimization

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

• Optimizedata model design materialization strategies incremental processing and query paths to reduce unnecessarycomputetoken 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 assetsand 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 Snowflakedbt 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 Pythondbtmodels data models and technical designs.

• Evaluate and demonstrate new dataplatformand 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 F5s Business Code of Ethics and promptly report violations of the Code or other company policies.

• Perform other related duties as assigned.
  

Knowledgeskillsand 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 andproductionalizingdata products and pipelines for enterprise-scale governance reliability and operational use.

• Experience with Snowflakedbtor similar transformation frameworks and data ingestion and orchestration technologies; familiarity withlakehouseor 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 ofcloud platform concepts across Azure and/or AWS including storagecompute 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.

• Bachelors degree in Computer Science Data Engineering Software Engineering Mathematics Information Systems or equivalent combination of education and experience.

• 8 or more years of experience in data engineering data platform engineering analytics engineering or a related technical roleincluding demonstratedadvanced SQLproficiencyacross 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 Snowflakedbt 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 andproductionalizingdata products pipelines and platform capabilities for enterprise-scale reliability governance and sustained operational use.

• 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.

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 tuningSnowpipe Snowpark secure views data sharing role-based access controls and cost optimization.

• Experience withStreamlitor similar Python-based data application frameworks.

• Experience withdbt 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 managedcompute serverless services and secure data integration patterns.

• Experience with Databricks Spark Delta Lake orMLflowin 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.