F5
From F5's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
The role balances day-to-day pipeline engineering, automated testing, and observability with strong engineering fundamentals.
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 Data Engineer II in the Product Data Group plays an active, hands-on role in implementing, optimizing, and maintaining data models and pipelines for product data (telemetry, logs, alerts, metrics, etc.).
Working closely with senior engineers, data platform architects, and cross-functional product teams, this engineer helps transform raw system events into reliable, high-quality data assets. The role balances day-to-day pipeline engineering, automated testing, and observability with strong engineering fundamentals.
Primary Responsibilities
Pipeline Implementation & Optimization: Build, optimize, and maintain scalable data pipelines and models for diverse analytics and product use cases, adhering to established data architecture, security, and access standards.
Data Lifecycle & Schema Management: Implement schema evolution, versioning, automated backfills, and resilient retry mechanisms across streaming and batch pipelines.
Quality & Observability: Implement automated data quality checks, pipeline observability, and alerting to monitor pipeline health against established SLAs; partner with senior engineers on root cause analysis (RCA) and remediation workflows.
Operational Excellence: Apply best practices in automated testing, monitoring, and runbook documentation to ensure continuous, reliable pipeline operations.
Data Cataloguing & Documentation: Contribute to comprehensive technical documentation, metadata repositories, and data lineage mapping in collaboration with data stewards and domain owners.
Cross-Functional Collaboration: Participate in engineering design reviews, code reviews, and sprint ceremonies; collaborate with software engineering teams to align on event schema design, source systems, and data contracts.
Desired Skills & Qualifications
While all these skills are important, it isn’t critical that they all be met exactly. If you think you’re a fit for this role, please apply!
Be kind.
Experience Level: 2–4 years of progressive hands-on experience in data engineering, preferably working with product telemetry, system logs, or event streams.
Data Modeling & SQL: Proven experience in database design, data modeling (relational, dimensional, or semi-structured), and SQL query performance optimization.
Pipeline Development: Solid experience designing and maintaining reliable batch and streaming data pipelines (ETL/ELT) using languages such as Python or SQL.
Testing & Quality Assurance: Hands-on experience implementing automated pipeline testing, data validation frameworks, and data quality checks.
Software Engineering Best Practices: Experience working with Git version control, CI/CD pipelines, containerization, and modern agile software development processes.
Governance & Metadata: Familiarity with data cataloguing, metadata management, and operating within data governance frameworks.
Collaboration & Problem Solving: Strong communication skills, with an ability to clearly explain technical issues and collaborate across distributed engineering teams.
Bonus Skills (Nice-to-Have)
Hands-on experience with modern data stack tooling: dbt, Databricks (including Unity Catalog), and AWS cloud data services.
Experience with distributed processing frameworks (e.g., Apache Spark) or event-streaming systems (e.g., Kafka).
Exposure to supporting data preparation and feature engineering for machine learning (ML) models.
#LI-SS5
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 accommodations@f5.com.
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