Data Architect Job at Valtech (Bengaluru)

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

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