—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)
—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 strategy — prioritizing domains and data products based on business value, dependency mapping, and technical readiness
—Establish lifecycle management policies for schema evolution, deprecation, and backward compatibility — enforced through Unity Catalog or equivalent cataloguing tooling
—Implement or oversee data quality frameworks (Great Expectations, dbt tests, Databricks Delta constraints) aligned to governance thresholds
—Provide hands-on architectural oversight during foundational delivery phases (Milestone 1 and equivalent programme gates) — ensuring the build conforms to the agreed architecture and standards
—Conduct architecture reviews and code/design walkthroughs with engineering squads — identifying deviations, technical debt, and remediation paths
—Chair Architecture Review Board (ARB) sessions with client technical leadership — presenting architecture decisions, trade-offs, and risk assessments
—Define and track architecture fitness functions — measurable criteria that validate the architecture is being implemented correctly across squads
—Serve as the escalation point for cross-squad architectural decisions, integration conflicts, and technology blockers
—Lead client architecture workshops, data strategy sessions, and roadmap definition exercises at executive and technical levels
—Produce and present strategic client-facing artefacts: Data Platform Vision documents, Architecture Blueprints, Governance Frameworks, and Capability Roadmaps
—Contribute to pre-sales and bid activities — leading the data architecture strand of RFP/RFI responses, solution sizing, and pitch presentations
—Represent Valtech's data architecture capability externally — through client advisory conversations, partner events, and thought leadership content.
—Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or equivalent professional experience
—10+ years of progressive experience in data architecture, data platform engineering, or enterprise BI/analytics roles
—Demonstrable experience as a lead data architect on at least two large-scale enterprise data platform programmes
—3NF — operational and transactional layer design
—Dimensional modelling — Kimball star/snowflake, slowly changing dimensions
—Data Vault 2.0 — hubs, links, satellites, business keys
—Data mesh & domain-oriented data product design
—Architecture Decision Records (ADRs) and C4 modelling
—Data contract design — schema, SLAs, versioning
—Data catalogue & metadata management design
—Data quality framework design and governance policy
—BigQuery — advanced design, partitioning, clustering, BI Engine
—Dataflow (Apache Beam) — streaming and batch architecture
—Cloud Composer / Airflow — orchestration design
—Pub/Sub — event-driven data product patterns
—Dataplex — data cataloguing, lineage, data quality
—Vertex AI Pipelines — ML feature pipeline integration
—Cloud Storage — data lake zone design (raw/curated/serving)
—Terraform — IaC standards and landing zone design
—Databricks Lakehouse — medallion architecture, zone design
—Delta Lake — ACID, time travel, schema evolution, Z-ordering
—Unity Catalog — governance, lineage, access control, data products
—Databricks Workflows and Asset Bundles — lifecycle automation
—PySpark — advanced optimization, broadcast, AQE, dynamic pruning
—dbt on Databricks — transformation layer standards
—Databricks on GCP — cluster policy, instance pools, cost governance
—MLflow — model registry integration with data product layer
—Unity Catalog — fine-grained access, row/column security
—Dataplex — data mesh governance on GCP
—Data lineage — column-level lineage design and tooling
—GDPR, data residency, and privacy-by-design principles
—Data stewardship operating models
—Metadata standards — Dublin Core, OpenLineage, OpenMetadata
—Google Cloud Professional Data Engineer or Professional Cloud Architect certification
—Databricks Certified Professional Data Engineer or Databricks Certified Associate Developer for Apache Spark
—TOGAF, Zachman, or equivalent enterprise architecture framework certification.
—Azure Synapse / Microsoft Fabric — cross-cloud architecture experience
—Apache Iceberg or Apache Hudi — open table format evaluation and migration experience
—Event-driven architecture — Apache Kafka, Confluent Cloud, or Pub/Sub advanced patterns
—Generative AI integration patterns — RAG pipelines, vector stores (AlloyDB, Vertex AI Vector Search), LLM-as-data-product
—Exposure to composable commerce data models — product, pricing, inventory, order domains (commercetools, SFCC, SAP Commerce)
—Experience in luxury, retail, or FMCG verticals — multi-brand data architectures, PIM/DAM integration patterns
—OpenMetadata or Collibra — enterprise-grade data catalogue implementation
—Familiarity with Salesforce Marketing Cloud data connectors or Customer Data Platforms (CDPs)
—Google Cloud Professional Machine Learning Engineer
—CDMP (Certified Data Management Professional) — DAMA certification
—HashiCorp Terraform Associate

TECHNICAL_SPECS.JSON

"technology_tags": [

"GCP",

"BigQuery",

"Dataflow",

"Cloud Composer",

"Pub/Sub",

"Dataplex",

"Vertex AI Pipelines",

"Cloud Storage",

"Terraform",

"Databricks",

"Delta Lake",

"Databricks Unity Catalog",

"Databricks Workflows",

"Databricks Asset Bundles",

"PySpark",

"dbt",

"MLflow",

"Great Expectations",

"Azure",

"Apache Iceberg",

"Apache Hudi",

"Kafka",

"RAG",

"Architecture",

"API Design",

"Microservices",

"Google Cloud Platform",

"Apache Beam",

"Apache Airflow",

"Unity Catalog",

"Asset Bundles",

"GDPR",

"Dublin Core",

"OpenLineage",

"OpenMetadata",

"Azure Synapse",

"Microsoft Fabric",

"Apache Kafka",

"Confluent Cloud",

"AlloyDB",

"Vertex AI Vector Search",

"Collibra",

"Salesforce Marketing Cloud",

"Domain-Driven Design",

"C4 modelling",

"Architecture Decision Records",

"3NF",

"Kimball",

"Data Vault 2.0",

"TOGAF",

"Zachman"

]

CULTURE_&_PERKS

Health & Wellness

Private health insurance provided to all employees — because your wellbeing matters.

Learning Budget

An education program with training and certification opportunities so you never stop growing.

Wellbeing Program

A dedicated wellbeing program because living a healthy, well-balanced life isn't optional — it's a necessity.

Free Food & Drinks

Free coffee, drinks and snacks at the office, plus legendary company dinners to look forward to.

Social Activities

Ski trips, karting, laser tag, wine tasting, picnics, cooking classes — we've done it all and we're not stopping.

Generous Holiday

24 days of paid vacation plus annual company events to recharge and celebrate together.

Flexible Hours

Flexible work schedules so you can do your best work whenever and wherever that looks like for you.

Hybrid Working

Work from our global offices, from home, or wherever you perform best — the choice is yours.

International Projects

Work alongside cross-functional, multi-cultural teams on global projects for brands like Ford, Mars, L'Oréal and Bose.

Cross-Cultural Team

A truly global team spanning 20+ countries — you'll always find someone to connect with and learn from.

DEI Commitment

A structured D&I strategy with a Global Council, maturity index, leadership accelerator program, and annual public reporting.

Mentorship Program

The Accelerating into Leadership programme pairs underrepresented talent with senior leaders for visibility, feedback and advocacy.

Career Progression

Room to start new business units, programs and skill centres — entrepreneurial growth is baked into how we work.

Lead - Data Architect

ROLE_OVERVIEW

TECHNICAL_SPECS.JSON

HIRING_PROCESS

CULTURE_&_PERKS

MORE_FROM_VALTECH

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