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:  Bachelors/masters 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.