DATA ARCHITECT - Data Architecture

Job Title: Data Architect Years of Exp : 12 to 14 years Job Description: 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 12-14 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.