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Data Architect

valtech.com Logo

Valtech

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Location:
India , Bengaluru

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Contract Type:
Not provided

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Salary:

Not provided

Job Description:

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.

Job Responsibility:

  • 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

Requirements:

  • 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

Nice to have:

  • 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
What we offer:
  • Flexibility, with remote and hybrid work options (country-dependent)
  • Career advancement, with international mobility and professional development programs
  • Learning and development, with access to cutting-edge tools, training and industry experts

Additional Information:

Job Posted:
May 15, 2026

Employment Type:
Fulltime
Work Type:
On-site work
Job Link Share:

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