Job Title: Data Architect
Work Model: Hybrid / Onsite
Location: Alameda CA
Experience Required: 8–12 Years
Job Summary
We are seeking an experienced Data Architect / Business Relationship Manager to support strategic, in-flight Data & Analytics initiatives. This role will work closely with business stakeholders, delivery teams, architecture, and engineering partners to build strong data foundations and drive Data, Analytics, AI, and Generative AI initiatives.
The ideal candidate will combine strong business relationship management, data architecture, data product strategy, and technical leadership skills, with particular experience in modern data platforms such as Databricks and Lakehouse technologies.
Key Responsibilities
Strategic Business Partnership & Cross-Functional Alignment
Serve as a trusted Data & Analytics leader supporting business functions in identifying opportunities to leverage Data, Analytics, AI, and Generative AI.
Build strong relationships with stakeholders across Commercial, Marketing, R&D, Quality, Operations, Supply Chain, Finance, and other business functions.
Understand business priorities, challenges, and strategic objectives and translate them into actionable technology and data initiatives.
Lead cross-functional discussions to drive prioritization, decision-making, and execution alignment.
Data Product Leadership
Establish, scale, and mature the Data Product delivery model across the organization.
Champion product-centric ways of working and promote Data Product principles across business domains.
Partner with stakeholders to identify, define, prioritize, and govern Data Products that deliver measurable business outcomes.
Drive Data Product discovery, visioning, roadmap development, lifecycle management, and value realization.
Define KPIs and success metrics to measure business impact.
Promote reusable, scalable, and governed Data Products.
Support Data Mesh principles, including domain-oriented data ownership and product-based data delivery.
Data Engineering & Platform Excellence
Champion modern Data Engineering principles and best practices.
Help build strong data foundations focused on:
Data Quality
Data Observability
Metadata Management
Data Lineage
Security
Governance
Reusable Data Assets
Partner with Architecture and Data Engineering teams to ensure enterprise data assets are scalable, reliable, secure, governed, and optimized for Analytics and AI workloads.
Drive adoption of Databricks and modern Lakehouse technologies supporting Data Products, Analytics, AI, and Generative AI.
Technical Leadership & Solution Architecture
Provide technical leadership across Data, Analytics, AI, and Data Product initiatives.
Guide initiatives from concept through delivery and business value realization.
Translate business requirements into scalable Data and Analytics solutions.
Partner with Enterprise Architecture and Corporate IT teams on solution strategies, architectural direction, platform adoption, and technology decisions.
Evaluate solution options and trade-offs based on business value, scalability, maintainability, security, and technical feasibility.
Ensure solutions are scalable, secure, governed, reusable, and aligned with intended business outcomes.
AI & Generative AI Leadership
Identify, assess, prioritize, and support the development of AI and Generative AI opportunities.
Partner with business stakeholders to define high-value use cases, expected outcomes, and success metrics.
Evaluate opportunities based on strategic value, feasibility, adoption readiness, and compliance requirements.
Collaborate with Legal, Privacy, Cybersecurity, Data Science, Engineering, Architecture, and Delivery teams.
Promote responsible AI practices and monitor adoption, business impact, and value realization.
Portfolio Prioritization & Demand Management
Partner with Corporate IT, Architecture, Engineering, and business stakeholders to ensure successful delivery of strategic initiatives.
Lead intake, evaluation, and prioritization of Data, Analytics, AI, and Generative AI initiatives.
Facilitate investment trade-off discussions and portfolio decisions.
Maintain visibility into project progress, risks, dependencies, resources, and expected outcomes.
Ensure investments and delivery capacity remain focused on the highest-value opportunities.
Provide portfolio recommendations and planning insights to business and Data & Analytics leadership.
Business-Led Data Governance & Stewardship
Champion a business-led Data Governance model focused on ownership, accountability, and stewardship.
Establish and support Data Ownership, Data Stewardship, Governance, and Data Quality practices across business domains.
Promote Metadata Management, Data Cataloging, Governance, and Compliance best practices.
Advocate for trusted, governed, high-quality, and AI-ready data assets that support strategic business objectives.
Required Skills
8–12 years of experience in Data Architecture, Data & Analytics, Business Relationship Management, or related roles.
Strong experience with Data Architecture and Data Engineering concepts.
Experience with Databricks and modern Lakehouse architectures.
Strong understanding of Data Products and Data Mesh principles.
Experience translating business requirements into Data, Analytics, and technology solutions.
Knowledge of Data Governance, Data Quality, Metadata Management, and Data Lineage.
Experience working with cross-functional business and technology stakeholders.
Strong portfolio management, prioritization, and stakeholder management skills.
Knowledge of AI and Generative AI use cases and delivery models.
Excellent communication, leadership, and relationship-building skills.
Preferred Skills
Experience in Healthcare, Medical Devices, Pharmaceuticals, or other regulated industries.
Experience supporting enterprise-wide Data & Analytics transformation initiatives.
Knowledge of Responsible AI, Privacy, Cybersecurity, and Compliance requirements.
Experience with enterprise Data Governance and Data Product operating models.

Data Architect

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