MS Fabric Data Architect
Description
Hi All,
I have opportunity for Data Architect .
Location Bangalore Hyderabad Mumbai
Immediate Joiners
Hybrid Model :- 2days working from Office.
Hyderabad Gachibowlli|| JP Nagar Banergatta| Mumbai Andheri Seepz
Data Architect | Data, Knowledge & AI Platforms
Experience: 1015 Years | Microsoft Fabric & Azure | End-to-End Ownership
ROLE PURPOSE
Own the architecture and delivery of an enterprise Data & Intelligence Platform, from source ingestion through trusted data, semantic/knowledge layers and consumption by analytics, GenAI and AI agents. The role combines hands-on technical leadership with architecture governance and business engagement, with Microsoft Fabric as a strategic platform rather than the boundary of the role.
KEY RESPONSIBILITIES
- Own target architecture and delivery across source systems, APIs/events, ingestion, lakehouse/warehouse, semantic models, knowledge layer and AI consumption; drive solutions from concept through production and operations.
- Architect and guide implementations using Microsoft Fabric (OneLake, Lakehouse, Warehouse, Data Factory, Spark, pipelines, semantic models/Power BI) and Azure technologies including ADF, ADLS and Synapse.
- Design scalable batch, streaming, CDC, ETL/ELT and metadata-driven integration patterns; define reusable engineering standards, CI/CD, observability, resilience and cost controls.
- Build the enterprise Knowledge Layer: business glossary, metadata, end-to-end lineage, ontology, semantic relationships, critical data elements and knowledge graphs (e.g., Neo4j).
- Enable AI readiness through governed grounding patterns for structured and unstructured data, RAG/agentic use cases, vector/hybrid search, metadata enrichment and traceable enterprise context.
- Design API and integration architecture covering REST/JSON, OAuth/OIDC, API management, versioning, throttling, retries, webhooks and event-driven patterns.
- Embed security and governance by design: Entra ID, RBAC/least privilege, managed identities, Key Vault, encryption, row/column/object-level controls, PII classification, masking, sensitivity/retention and auditability.
- Lead architecture/design reviews, mentor engineers, challenge implementation choices, drive PoCs and partner with business, security, governance, application, BI and AI teams.
REQUIRED EXPERIENCE & CAPABILITIES
- 10–15 years in data engineering/platforms/architecture, including 3–5+ years of architecture or technical leadership and proven ownership of enterprise-scale production platforms.
- Strong hands-on depth in Microsoft Fabric, Azure Data Factory, Synapse, Data Lake, SQL, Spark/PySpark, lakehouse/warehouse architecture, data modelling and CI/CD.
- Strong understanding of conceptual/logical/physical modelling, dimensional and Data Vault concepts, medallion/data-product patterns, master/reference data and structured/semi-structured/unstructured data.
- Working knowledge of Microsoft Purview or equivalent, cataloguing, lineage, business glossary, data quality, stewardship, ontology/semantic modelling and graph concepts; Neo4j or equivalent is desirable.
- Architectural understanding of GenAI, LLMs, RAG, AI agents, embeddings, vector/hybrid retrieval and the controls required for trusted, permission-aware AI consumption.
- Ability to communicate with senior business/technology stakeholders while remaining sufficiently hands-on to review SQL, Spark, APIs, pipelines, security and data models.
SUCCESS PROFILE
The ideal candidate can move seamlessly between “What business capability are we enabling?” and “Show me the data model, API, pipeline and security design.” Success is measured by a scalable, secure and governed platform where enterprise data becomes discoverable, semantically understood, connected and reliably consumable by people, BI and AI.
Skills
- Agentic AI
- AI
- Analytics
- API
- Azure
- Azure Data Factory
- CI/CD
- Data Engineering
- Data Lake
- Data Modeling
- Data Quality
- Data Vault
- ELT
- Embeddings
- Entra ID
- ETL
- Event Driven Architecture
- Generative AI
- JSON
- Lakehouse
- LLM
- Microsoft Fabric
- Microsoft Purview
- Neo4j
- OAuth
- Observability
- OpenID
- Power BI
- PySpark
- RBAC
- Spark
- SQL
- Vault
- Webhooks