Lead - Data Architect

Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values -driven culture, international careers and the chance to shape the future of experience. The opportunity At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries. We are proud of: The work we do and the innovation we drive Our values of share, care a nd dare A workplace culture that fosters creativity, diversity and autonomy Our borderless, global framework, which enables seamless collaboration The role As a Data Architect , you are passionate about experience innovation and eager to push the boundaries of what’s possible. You bring 10+ YEARS of experience, a growth mindset and a drive to make a lasting impact. You will thrive in this role if you are: A curious problem solver who challenges the status quo A collaborator who values teamwork and knowledge-sharing Excited by the intersection of technology, creativity and data Experienced in Agile methodologies and consulting (a plus) Role responsibilities Define and document the end-to-end target-state data architecture for enterprise client programmes — covering ingestion, storage, transformation, serving, and consumption layers Establish domain-driven data architecture boundaries aligned to business domains (e.g. Product, Customer, Order, Finance) using Domain-Driven Design (DDD) principles Lead architecture design sessions with clients to align on technology choices, topology, and migration sequencing Produce architecture artefacts to a consultancy standard: C4 diagrams, data flow diagrams, architecture decision records (ADRs), and technology selection rationale documents Evaluate and recommend GCP-native vs. third-party component trade-offs — with clear cost, scalability, and maintainability justification Define and enforce enterprise data modelling standards across the programme — covering 3NF (operational layer), dimensional modelling (Kimball star/snowflake for analytics), and Data Vault 2.0 (historized, auditable Lakehouse layers) as appropriate Establish canonical data models for core business domains — ensuring consistency across squads and brands or business units Design and govern the metadata framework: schema standards, naming conventions, entity definitions, data dictionaries, and lineage documentation Oversee data contract design between producing and consuming domains — defining SLAs, schemas, versioning, and change management protocols Ensure models are optimized for the target query engine — BigQuery partitioning/clustering strategies, Delta Lake Z-ordering, and Databricks Photon engine considerations Design reusable, domain-oriented data product patterns — encapsulating ingestion, transformation, quality, and serving logic as deployable, versioned units Define the data product interface contract: output ports (APIs, tables, streams), SLOs, ownership, and discoverability metadata in Unity Catalog or Dataplex Establish a data product taxonomy aligned to business capability domains — enabling a self-serve data mesh posture for mature clients Create accelerators and reference implementations that mid-level engineers can adopt — reducing bespoke build and enforcing consistency Collaborate with Data Science and Analytics Engineering teams to ensure feature stores and ML feature pipelines are aligned to the broader data product architecture Drive data governance strategy across the programme — defining policies for data classification, access control, retention, and quality thresholds Design the governance operating model: data stewardship roles, data ownership accountability (domain owners vs. platform owners), and escalation paths Define the data release sequencing