Senior Staff Data Architect

Infoblox•Olympia, WA

3d•Hybrid

About The Position

At Infoblox, every breakthrough begins with a bold “what if.” What if your ideas could ignite global innovation? What if your curiosity could redefine the future? We invite you to step into the next exciting chapter of your career journey. Bring your creativity, drive, your daring spirit, and feel what it’s like to thrive on a team big enough to make an impact, yet small enough to make a difference. Our cloud-first networking and security solutions already protect 70% of the Fortune 500 , and we’re looking for creative thinkers ready to push that influence even further. Join us and discover how far your bold “what if” can take the world, your community, and your career. How we empower our people is extraordinary: we’re recognized as a Glassdoor Best Place to Work 2025, Great Place to Work-Certified in five countries, and honored by Cigna as a Healthy Workforce honors for three consecutive years; and what we build is world class: named CybersecAsia’s Best in Critical Infrastructure 2024 — clear evidence that when first-class technology meets empowered talent, remarkable careers take shape. So, what if the next big idea, and the next great career story, comes from you? Become the force that turns every “what if” into “what’s next.” In a world where you can be anything, Be Infoblox. Staff Data Engineer We have an opportunity for a Senior Staff Data Architect to join our IT Data, Reporting & Analytics team in Home Office - NJ, reporting to the Senior Director, Data Analytics & Reporting - IT Executive. In this pivotal role, you will design, build, and maintain the scalable data pipelines and platform infrastructure that power Infoblox’s enterprise reporting, analytics, and AI-driven decision-making. Collaborating closely with Analytics Engineers, BI Developers, and cross-functional partners across Finance, Sales Operations, Customer Success, and IT, you will translate raw data from our enterprise application landscape into reliable, governed, and consumption-ready data products — and help elevate the maturity of a fast-growing data platform.

Requirements

10 plus years of professional experience in data engineering, analytics engineering, or a closely related discipline, with a track record of delivering production-grade pipelines and models
Hands-on proficiency with SQL and Python; comfortable writing, reviewing, and debugging production code in both languages
Practical experience with the modern data stack: cloud data warehouse (Databricks, Redshift, Snowflake, or equivalent), transformation frameworks (dbt or similar), orchestration (Airflow or equivalent), and ELT tooling (Fivetran, AWS Glue, or similar)
Demonstrated experience building pipelines that ingest from SaaS enterprise applications (e.g., Salesforce, Oracle Fusion) via REST APIs, CDC, or batch patterns
Working knowledge of data modeling concepts (dimensional, normalized, medallion/lakehouse) and the judgment to apply the right pattern for the right use case
Familiarity with data governance fundamentals: RBAC, data lineage, metadata management, data quality frameworks, and access review processes
BS/BA in Computer Science, Information Systems, Data Science, Engineering, or a related field; equivalent practical experience considered

Nice To Haves

Experience with AI/ML-adjacent workflows — preparing datasets for ML consumption, using AI coding assistants, or implementing automated anomaly detection — is a plus
Proven ability to work effectively in an Agile environment, manage competing priorities, and communicate clearly with technical and non-technical stakeholders

Responsibilities

Build and maintain robust ELT/ETL pipelines ingesting data from enterprise source systems (e.g., Salesforce, Oracle Fusion, Marketo, Zuora) into the data lake and warehouse, ensuring reliability, freshness, and SLA adherence
Develop and maintain data models across bronze, silver, and gold layers of the medallion architecture, applying dimensional, normalized, or data vault patterns as appropriate
Implement data quality checks, automated monitoring, alerting, and logging so pipeline failures and anomalies are detected by the platform — not discovered by the business
Maintain and improve the transformation layer using dbt (or equivalent), including model documentation, testing, lineage, and certification of data assets
Enforce data governance standards: naming conventions, metadata, access controls (RBAC/CBAC), data masking, and retention policies aligned with security and compliance requirements
Collaborate with Analytics Engineers and BI Developers to deliver consumption-ready semantic layer objects and reporting views that are accurate, performant, and reusable
Leverage AI-assisted tooling (e.g., AI code assistants, automated anomaly detection, LLM-based documentation generation) to accelerate development, improve data quality, and reduce manual toil
Participate in Agile sprint ceremonies, contribute to backlog refinement, and provide accurate effort estimates for data engineering work items
Support evaluation and adoption of new tools and patterns in the modern data stack, providing evidence-based input on build/buy/adopt decisions
Contribute to a culture of engineering excellence by writing clean, well-documented, testable code and participating in peer code reviews