Happiest Minds Technologies - Data Architect

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.