DATA ARCHITECT - Data Architecture

Key Responsibilities

• Enterprise reference architecture: Create, publish, and maintain the enterprise data platform reference architecture (principles, target-state, standards, patterns, and guardrails) to enable repeatable implementations across teams and operating companies.
• Architecture governance: Lead architecture review forums, document decisions (trade-offs, rationale, and approved patterns), and manage exceptions to ensure consistent adoption and controlled evolution of the architecture.
• Requirements-to-architecture translation: Convert business and technical needs into implementation-ready architecture designs, source-to-target mappings, and technical specifications; partner with BAs/PMs as needed while remaining accountable for the technical stack definition.
• Lakehouse/warehouse design: Define enterprise lake/Lakehouse/warehouse patterns (e.g., medallion/bronze-silver-gold), data product conventions, and semantic modelling approaches to support governed self-service BI and downstream AI consumption.
• Data modelling standards: Lead data modelling practices (e.g., dimensional, 3NF, Data Vault), define conformed entities and KPI/metric definitions, and establish semantic layer standards for consistent analytics.
• AI/ML enablement: Design data architectures that support AI/ML lifecycles, including feature-ready datasets, training/validation data management, experiment reproducibility, and scalable data feeds for model inference.
• GenAI & unstructured data: Define patterns for unstructured/semi-structured data (documents, images, logs) including extraction, enrichment, indexing, and governance for retrieval-augmented generation (RAG) and knowledge experiences.
• Data governance & quality: Partner with governance teams to define data domains, ownership, stewardship, glossary, lineage, and data quality controls; establish certification/curation processes for authoritative datasets.
• Security & compliance by design: Define and enforce controls for data classification, access (RBAC/ABAC), encryption, retention, auditing, and privacy-by-design (including PII/PHI where applicable).
• Integration reference patterns: Define integration patterns for APIs, events, CDC, and batch ingestion; ensure interoperability across source systems and downstream consumers.
• POC and rollout playbook: Drive reference architecture proof-of-concepts, codify learnings into standards, and create rollout/enablement assets (templates, checklists, runbooks) for scaled adoption.
• Operational excellence: Establish monitoring and operational patterns (SLAs/SLOs, data observability, incident/runbook standards) and guide teams on performance and cost optimization.
• Stakeholder leadership: Communicate architecture decisions to technical and non-technical audiences; mentor engineers/architects and elevate architectural maturity across the organization.

Educational qualification: Bachelor?s/master?s degree in computer science, Information Systems, Engineering, or a related field (or equivalent practical experience).

Experience: 8+ years of experience in data engineering, analytics engineering, platform engineering, or data architecture, including ownership of enterprise data platform designs. Total exp should be 12-16 years

Skills Required

• Strong Microsoft data platform experience, including Microsoft Fabric (One Lake, Lakehouse/Warehouse, Pipelines, Notebooks) and/or Azure Data Factory, Azure Synapse, Azure SQL, and Power BI semantic modelling.
• Experience defining a Fabric/Azure reference architecture (networking, identity, workspaces/capacity strategy, Dev/Test/Prod separation, CI/CD, monitoring, cost management) and guiding implementation teams through adoption.
• Experience with Delta Lake / Parquet, partitioning strategies, and performance optimization for large-scale datasets.
• Experience with data catalog and governance tooling (e.g., Microsoft Purview) including lineage and metadata strategies.
• MLOps/LLMOps familiarity (CI/CD for data + ML, model deployment patterns, monitoring/drift, reproducibility).
• Experience designing data solutions for RAG and vector search (embedding generation workflows, document chunking strategies, evaluation approaches), aligned to security and compliance requirements.
• Experience in using Gen AI tools on designing, architect solution and day to day activities.
• Experience with DevOps practices (Git, automated testing, infrastructure-as-code) and operating in Agile/Hybrid delivery models. Relevant certifications (e.g., Azure Solutions Architect, Azure Data Engineer, Fabric Analytics Engineer) are a plus. Data Architecture, Azure Service Fabric, Azure Data Factory, Azure Power BI