Data Architect-Senior Manager

About The Role

PwC’s Data & AI Consulting team is rapidly expanding as we invest in building a new generation of Artificial Intelligence (AI) products that transform how we deliver value to our clients. We’re recognised by industry analysts, such as Gartner and IDC, as a market-leading Data & AI services consultancy and are actively working with clients to design, develop and deliver AI-powered products and data capabilities that achieve tangible outcomes and business value.

We’re looking for self‑starting, progressive, and inquisitive individuals who want to shape the future of how AI is applied in real business contexts. You’ll join a collaborative and entrepreneurial team that combines deep technical expertise with sector knowledge and product thinking. We work in cross‑functional squads to design, build, and launch solutions that create measurable impact for our clients and strengthen PwC’s position as a leader in trusted, responsible AI.

If you want to apply your skills to complex challenges, help define new products, and be part of an ambitious team that’s re‑imagining the role of AI in professional services, this could be the role for you.

• Leading the design of enterprise data architectures that underpin modern analytics, AI, and digital transformation programmes – shaping how data is ingested, modelled, governed, and served across cloud platforms.
• Defining end‑to‑end data architecture strategies, covering integration approaches, data modelling patterns, lineage, quality, metadata management, and master data management, ensuring solutions are secure, scalable, and aligned to enterprise vision.
• Conducting architectural discovery, shaping data work streams, and defining technical delivery approaches for client programmes across Azure, AWS, GCP or Snowflake environments.
• Working as part of cross‑functional product squads – including Data Engineers, Data Strategist, AI specialists, and Business SMEs – to design, build, and provide technical assurance for data‑driven architectures.
• Partnering with client stakeholders to understand business challenges, define data requirements, evaluate technology options, and shape investment decisions through clear articulation of value and return on investment.
• Collaborating closely with engineering teams to translate data architecture designs into buildable solutions, overseeing modelling, pipelines, integration patterns, and platform services through to delivery.
• Contributing to data strategy, architectural roadmaps, and platform evolution, helping clients modernise their data landscape and strengthen their analytics and AI capabilities.

• Proven experience in enterprise architecture, including full lifecycle experience designing and delivering platform‑agnostic data architectures, leading cross‑functional teams to build data models, pipelines, and semantic layers that drive business value.
• Strong commercial awareness, with the ability to link data modelling decisions to business outcomes, sector‑specific challenges, and broader market trends.
• Experience selecting data and integration technologies at project or enterprise level, and confidence in articulating pros, cons, and ROI to client stakeholders.
• Deep understanding of data governance, lineage, quality frameworks, and master data management, and their role within a modern data platform.
• A track record of delivering high‑quality client engagements and building trusted relationships with both internal and external stakeholders.
• Strong consulting foundations: impactful communication, structured problem‑solving, adaptability, and the ability to navigate ambiguity and deliver under pressure.
• Design expertise in core data architecture disciplines including data modelling (proficient in at least one data modelling tool e.g. Erwin or ER Studio), metadata management, data migration and master data management.
• Well versed in data architecture approaches to ingest, model and serve data from various sources including ERP (Oracle, SAP etc.), CRM (Salesforce, etc.) and HR systems (Workday etc.) and practical knowledge of core data ETL and visualisation skills – for example…