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A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

Our Enterprise Data & Analytics (EDNA) organization serves as the backbone for data-driven decision-making across Equinix's $10B+ global business. We are transforming from fragmented, ad-hoc analytics to a unified, self-service platform that empowers analytics communities. The scope spans enterprise semantic layers, dimensional data models, analytics-ready "Explores," metric catalogs, and self-service governance frameworks. These solutions help business leaders improve decision speed, eliminate metric inconsistencies, optimize analyst productivity, reduce duplicative work, and unlock advanced analytics capabilities (ML, GenAI, embedded analytics). Over time, we aspire to establish world-class data product practices that enable Equinix to compete on analytics maturity with leading digital-native companies.

We are looking for a Director of Data Product Management—Enterprise Business Intelligence to lead product roadmap for self-service BI. This role partners closely with functional analytics leaders to understand strategic priorities, operating rhythms, and pain points such as fragmented data models, manual reconciliations, data wrangling overhead, and inconsistent metrics. You will translate these into high-impact data products that consolidate duplicative models, enable self-service analytics, establish metric governance, and position EDNA as a platform enabler rather than a bottleneck. The role will drive resource prioritization, ROI-based roadmap decisioning, and collaboration with Data Engineering, Business Systems Analysts, and IT teams to deliver scalable, governed, and user-friendly analytics infrastructure.

Responsibilities

Data Product Strategy, Roadmap, and Management:

• Build deep trusted advisor relationships with functional analytics leaders by understanding their strategic objectives, operating cadence, and analytics workflows

• Translate analytics pain points, such as data wrangling overhead, metric inconsistencies, duplicative models, slow time-to-insight, and EDNA backlog frustration, into semantic layer product solutions

• Develop multi-year Semantic Layer Product Strategy aligned with EDNA goals and functional priorities.  Roll out a phased delivery plan consolidating 200+ models into <50 governed Subject Areas over 18-24 months

• Manage product lifecycle: launch new Subject Areas, iterate based on usage feedback, deprecate legacy models with clear migration paths and sunsetting timelines

• Communicate value realization: establish and report on KPIs including self-service adoption rate (target: 70%+), backlog reduction (target: <3 months), analyst productivity shift (target: 70% analysis vs. 30% wrangling), model consolidation progress, and platform NPS

Semantic Layer Architecture & Technical Strategy:

• Define end-to-end semantic layer architecture, from source systems through bronze/silver/gold data layers to semantic layer to consumption layer, with clear separation of concerns and modern data stack best practices

• Establish technical standards for dimensional modeling: star schema design patterns, slowly changing dimensions, grain management, conformed dimensions, aggregation strategies, and performance optimization techniques

• Design tiered self-service model with clear governance boundaries: Tier 1 (enterprise certified metrics), Tier 2 (function-specific certified), Tier 3 (experimental self-serve), defining technical implementation, access patterns, and promotion workflows for each tier

• Drive technology selection and architectural decisions: evaluate and recommend semantic layer tooling (dbt, LookML, Cube, AtScale), BI platform consolidation strategy (Looker vs. Tableau vs. Power BI), and integration patterns with BigQuery

Self-Service Data Product Development:

• Develop and manage EDNA self-service data product portfolio: define portfolio boundaries, establish prioritization framework balancing impact vs. effort. Make sequencing decisions on Subject Area build order, model consolidation priorities, and legacy product sunsetting. Regularly rationalize portfolio based on usage and ROI.

• Lead development of Subject Area "Explores" as analytics-ready data products with clear product ownership, SLAs, documentation, versioning, and deprecation policies

• Design metric catalog and discovery layer: searchable catalog with metric definitions, lineage, ownership, certification status, usage analytics, and data quality scorecards to enable self-service discovery

Platform Enablement & Self-Service Strategy:

• Define federated operating model: clarify ownership boundaries between central EDNA and functional teams

• Build enablement infrastructure: training programs, office hours, reference documentation, starter templates, example implementations, and troubleshooting guides to scale self-service adoption

• Instrument platform health monitoring: track usage metrics, adoption rates, query performance, cost efficiency, data quality, user satisfaction (NPS), and time-to-insight to drive continuous improvement

Qualifications

• Degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, or a related quantitative field

• Product management: Roadmap prioritization, stakeholder management, ROI analysis, iterative delivery, usage instrumentation, continuous improvement

• Deep technical expertise in semantic layer architecture: hands-on experience building enterprise semantic layers using modern data stack (dbt, LookML/Cube, cloud data warehouses like BigQuery/Snowflake/Databricks)

• Proven track record building self-service analytics platforms at Fortune 500 scale (1,000+ users), with demonstrated success driving adoption, reducing bottlenecks, and shifting teams from fulfillment to enablement models

• Strong technical leadership: ability to evaluate and recommend technology stacks, design scalable architectures, establish technical standards, and mentor analytics engineers

• 18+ years of industry expereince and 8-10+ years in Analytics Engineering, Data Product Management, or BI/Analytics Platform leadership, ideally with experience leading semantic layer initiatives, metric governance programs, or analytics infrastructure transformations

• Excellent communication and interpersonal skills, with proven track record building trusted advisor relationships with business stakeholders and driving adoption of new platforms in resistant environments

• Proven ability to lead cross-functional teams across Analytics, Data Engineering, and Business functions

• Dynamic, forward-thinking leader with bias for action, rapid experimentation, and ability to architect novel solutions to ambiguous problems

• Preferred: Prior experience in Data Center, Cloud, Telecommunications, Infrastructure, or B2B technology industries

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability.  If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer.  All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law. 

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