Data Architect

Job Description – Data Architect

Data Architect – Data & Insights

Summary

We are looking for a highly experienced Data Architect (Grade 7A) to lead the design and delivery of the enterprise Data & Insights platform on Microsoft Azure . The role demands deep expertise in cloud data architecture , data models , data lake/lakehouse , vector-store based RAG systems , GenAI , Agentic AI , and governance using Microsoft Purview .

The ideal candidate has strong hands-on skills in Python , LangChain , LangGraph , Azure data services, and end-to-end SDLC execution.

Roles & Responsibilities

• Architect and design Azure-based data lake/lakehouse platforms , domain data models, and ingestion-to-consumption pipelines.
• Develop conceptual, logical, and physical cloud data models aligned with enterprise standards.
• Architect RAG pipelines including embeddings, chunking, vector stores, hybrid retrieval, reranking, and evaluation.
• Build Agentic AI workflows using LangChain and LangGraph; design tool orchestration, memory, and safety layers.
• Implement governance with Microsoft Purview for cataloging, lineage, PII tagging, and policy enforcement.
• Ensure platform security using Entra ID, private endpoints, VNETs, Key Vault, and encryption controls.
• Lead solution architecture reviews, performance tuning, cost optimization, and NFR engineering.
• Oversee CI/CD (Azure DevOps), IaC (Terraform/Bicep), and observability (Azure Monitor, App Insights).
• Mentor engineering teams and standardize best practices, patterns, and reusable components.

Technical Skills

Mandatory

• Azure Data Platform : ADLS Gen2, Synapse/Serverless SQL, Databricks/Spark, ADF/Synapse Pipelines
• Programming : Python, PySpark, SQL
• GenAI & Agentic AI : RAG architecture, vector stores (Azure Cognitive Search, Pinecone, Weaviate, Qdrant), embeddings, reranking
• Frameworks : LangChain, LangGraph
• Data Modeling : Conceptual/logical/physical models, Delta/Parquet patterns, lakehouse modeling
• Data Governance : Microsoft Purview (catalog, lineage, classification, glossary, PII governance)
• Security : Entra ID, RBAC/ABAC, Key Vault, VNET integration, encryption
• SDLC & DevOps : Azure DevOps (CI/CD), Terraform/Bicep, ADRs, HLD/LLD documentation
• Performance & Cost Optimization across compute, storage, vector workloads, and pipelines

Preferred

• Azure Fabric / OneLake; Power BI semantic modeling
• dbt for transformations and testing
• Cosmos DB, PostgreSQL, SQL Server MI
• Knowledge graphs (Neo4j) and graph-based retrieval
• LLMOps: evaluation, telemetry, safety assessment, drift monitoring
• FinOps optimization practices
• Multi-cloud experience (AWS/GCP equivalents)
• API design: REST, GraphQL, gRPC

Qualifications

• Bachelor’s or Master’s degree in Engineering, Computer Science, or related discipline.
• 12–14 years of total experience with minimum 5+ years in cloud data architecture.
• Proven experience delivering Azure-based data platforms and production-grade GenAI/RAG systems .

Top 5 Screening Points for Recruiter

1. Strong Azure Data Platform Architecture Experience

Look for explicit, hands-on experience with Azure Data Lake Gen2 , Synapse/Serverless SQL , Databricks/Spark , and Azure Data Factory/Synapse Pipelines .

Keywords to match: ADLS, Synapse, Databricks, Spark, ADF, Delta Lake, Lakehouse.

2. Proven Expertise in GenAI, RAG & Vector Stores

Candidate must have real project experience building Retrieval-Augmented Generation (RAG) systems with vector databases.

Keywords to match: RAG, vector stores, embeddings, hybrid search, Azure Cognitive Search (vector), Pinecone, Weaviate, Qdrant, LangChain, LangGraph.

3. Solid Data Modeling & Architecture Background

Candidate should demonstrate ability to design conceptual, logical, physical data models , and lakehouse architectures.

Keywords to match: data modeling, dimensional modeling, canonical models, Delta/Parquet, partitioning, Z-order, architecture diagrams.

4. Microsoft Purview & Data Governance Experience

Specific experience implementing cataloging, lineage, PII tagging, and governance frameworks.

Keywords to match: Purview, data governance, lineage, catalog, data quality, PII/PHI classification, access policies.

5. Strong Python, PySpark, SQL + Hands-on Technical Delivery

Look for strong programming skills and end‑to‑end SDLC ownership.

Keywords to match: Python, PySpark, SQL, CI/CD (Azure DevOps), Terraform/Bicep, HLD/LLD, architecture reviews, full-lifecycle delivery.

Optional Recruiter Tip (Very Useful)

Reject profiles that only list:

'4C; “Azure”, “GenAI”, or “LangChain” without project details or outcomes .

Prioritize candidates who describe actual implementations , not tool familiarity.