Senior Staff Data Architect

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Senior Staff Data Architect - IT Data, Reporting & Analytics

We have an opportunity for a Senior Staff Data Architect to join our IT Data, Reporting & Analytics team in the US, reporting to the Senior Director - Data, Analytics and Reporting. In this role, you will own the target-state architecture for Infoblox's enterprise data estate: the data lake and warehouse, the core domain models, the semantic layer, the integration patterns that connect our source systems. You are the senior technical authority for how enterprise data is modeled, moved, governed, secured, and consumed, and you will set the standards that our data engineers, analytics engineers, and BI developers build against. You will partner closely with other Senior Directors in our AI and Enterprise Architecture spaces. This is a hands-on individual contributor role at the Senior Staff level. You will spend your time in design, in code, and in review: defining reference architectures, pressure-testing solution designs, prototyping patterns, and mentoring. Your influence will extend across IT, Finance, Sales Operations, Customer Success, Product, and Security, and your architecture decisions will determine whether Infoblox's data foundation can support the shift from reactive reporting to proactive, AI-enhanced decision intelligence.

Be a Contributor - What You'll Do

• Own and drive the enterprise Information Architecture for IT data: define the target-state data architecture, the reference patterns, and the multi-year roadmap that gets us there from current state
• Design conformed, reusable core domain data models (customer, product, order, subscription, entitlement, finance, support) and the semantic layer that makes them consistently consumable across BI, self-service, and AI tooling
• Define and enforce standards for data modeling, naming, metadata, lineage, documentation, testing, and certification of data assets, and continuously review adoption of those standards across complex solutions at scale
• Define and measure solution effectiveness: establish the metrics for pipeline reliability, data quality, freshness and SLA adherence, query performance, model reuse, and platform cost, and use them to drive architectural decisions rather than opinion
• Architect scalable, governed ingestion and integration across the modern data stack and Infoblox's application landscape (e.g., Databricks, FiveTran, dbt, Airflow, Oracle Fusion, Salesforce, Boomi)
• Pursue performance, maintainability, and efficiency relentlessly: identify and remove tech debt, redundant datasets, and low-value assets, and ensure remediation is planned into future increments rather than deferred indefinitely
• Design for security and compliance by default: RBAC and row/column-level access models, data sharing and masking patterns, data protection and retention, audit and logging; drive the quarterly access review process for data platforms
• Implement process automation, proactive monitoring, auditing, and logging techniques so that data quality and pipeline failures are detected by the platform, not by the business
• Contribute to the IT Program Roadmap from an estimation, requirements rationalization, and feasibility perspective, and partner with senior IT and business leaders on sequencing and trade-offs
• Assess the diversity and appropriateness of new technology patterns as they apply to Infoblox's application and data landscape, and make clear, evidence-based build/buy/adopt recommendations
• Architect the data foundation for AI: metadata quality, semantic definitions, and access patterns that make generative AI assistants, ML-driven anomaly detection, and self-service insight reliable
• Provide technical supervision and solution design guidance to multiple data and. Analytics engineers on the team; conduct design and peer code reviews and raise the technical floor of the group
• Coach and mentor less experienced analysts, analytics engineers, and developers, and act as the go-to person in the data domain on technical matters across multiple platforms
• Participate in Agile development scrums, and participate in and lead critical input to Release Retrospective sessions

Be Prepared - What You Bring

• Degree in Engineering, Computer Science, Information Science, Data Science, or equivalent; Master's degree a plus
• Typically requires 15+ years of previous professional experience, or 10+ years of previous professional experience and a Master's degree
• Advanced certification in relevant technical areas (e.g., SnowPro Advanced Architect, AWS Certified Solutions Architect / Data Engineer, CDMP, TOGAF, or platform-equivalent), plus professional association and/or membership with industry and community groups
• Demonstrated ownership of enterprise-scale data architecture: you have defined a target-state architecture, published the standards, and can point to what measurably improved as a result
• Deep, applied data modeling expertise (\ dimensional, normalized, and/or data vault) and the judgment to know which is appropriate where, including the metadata and downstream-impact implications of model change
• Hands-on proficiency with SQL and at least one general-purpose language (e.g., Python, Java); you can read, write, and review production code, not just diagrams
• Practical depth with the modern data stack: cloud data warehouse (Databricks or equivalent), transformation frameworks (dbt or similar), orchestration, cataloging and lineage tooling, and enterprise BI platforms
• Hands-on experience with application integration practices (SOAP/REST APIs, iPaaS (e.g., Boomi), CDC and batch patterns) and with the underlying data models of enterprise applications such as Salesforce, Oracle Fusion, or comparable ERP/CRM systems
• Working knowledge of system/application administration concerns: RBAC, data sharing models, visibility and reporting, and how access design affects both risk and analyst productivity
• Experience with security, audit, and compliance requirements as they apply to enterprise data, including access review, data protection, and data security and maintenance best practices
• Experience applying AI/ML to analytics and data platforms (e.g., anomaly detection, predictive models, or generative AI copilots for discovery, documentation, or reporting), with a clear-eyed view of where it works and where it does not
• Expert practitioner of Agile delivery and comfortable operating in CI/CD and release management practices
• Expert communicator able to lead large technical forums, influence outcomes without authority, and deliver presentations, reports, and analysis to senior stakeholders routinely; capable of acting as a business partner in prioritization, budgeting, and planning discussions
• Comfort with ambiguity and early-stage maturity; a track record of building structure and standards where few existed, and of challenging the status quo constructively

Be Successful - Your Path

First 90 Days: Immerse in our culture, connect with mentors (Blox Buddies), and map the systems, pipelines, models, and consumers that rely on your work. Produce a current-state assessment of the data architecture with a prioritized list of architectural risks, redundancies, and quick wins. Discuss and create short/long term goals.

Six Months:

• Publish and socialize the target-state enterprise data architecture, the reference patterns, and the modeling and metadata standards teams will build against
• Establish the architecture review mechanism for data solutions and embed it in Planning Increment and Architecture Runway activities
• Deliver at least one high-impact architectural change with measurable results (e.g., a conformed core domain model, a certified semantic layer, or a re-architected ingestion pattern) and retire the duplicate assets it replaces
• Baseline solution effectiveness metrics - pipeline reliability, SLA adherence, data quality, query performance, and platform cost - and put monitoring and alerting in place so they are tracked automatically
• Strengthen the access model and drive the quarterly access review process for data platforms in partnership with Security

One Year:

• Demonstrate sustained, quantifiable improvement against the effectiveness baseline (fewer duplicated datasets, higher SLA adherence, better performance at equal or lower cost) with the measurement embedded in normal workflow
• Establish the data architecture as genuinely governed: standards adopted by default, lineage and definitions trustworthy, and low-value assets deprecated on a predictable cadence
• Deliver an AI-ready data foundation, with semantic definitions and metadata of sufficient quality that AI-assisted discovery, documentation, and analytics produce reliable results
• Be recognized across IT and the business as the go-to technical authority for enterprise data, materially raising the design quality of work you did not personally build
• Shape the IT Program Roadmap through credible estimation, feasibility assessment, and technology evaluation, and mentor engineers into stronger architectural thinkers in code reviews and knowledge-sharing, raising the technical floor of the broader data engineering team