– Data Architect Consultant

Position: Data Architect – Consultant | Modern Data & AI Architecture

Experience: 10 years

Mode- Freelancer/ Consultant

Role Overview

We are looking for an experienced Data Architect Consultant to design scalable, secure and high-performance enterprise data architectures across cloud, data platforms, analytics and AI workloads.

The candidate will work with business, engineering, analytics and AI teams to define end-to-end data architecture, including data ingestion, integration, storage, processing, modeling, governance, security and consumption.

The role also requires the ability to design AI-ready data platforms supporting Generative AI, RAG, AI agents and advanced analytics.

Required Skills

Must Have

- 10 years of experience in Data Engineering / Data Architecture.

- Strong experience designing enterprise data architectures.

- Solid SQL and data modeling expertise.

- Experience with Data Warehouse / Data Lake / Lakehouse architectures.

- Strong experience with at least one major cloud platform – AWS, Azure or GCP.

- Experience with modern data platforms such as Databricks, Snowflake, Microsoft Fabric or BigQuery.

- Strong understanding of ETL/ELT, APIs, batch and streaming architectures.

- Strong understanding of data governance, security, quality and lineage.

- Experience working with senior business and technical stakeholders.

AI / GenAI – Required

- Understanding of AI-ready data architecture.

- Practical exposure to GenAI / LLM / RAG architectures.

- Understanding of vector databases and semantic/hybrid search.

- Understanding of how enterprise data is consumed by AI applications and agents.

Good to Have

- Data Mesh / Data Fabric experience.

- Apache Kafka / event-driven architecture.

- Apache Iceberg / Delta Lake.

- Microsoft Fabric.

- Databricks.

- Snowflake.

- Data Vault.

- Knowledge Graphs / Graph databases.

- Experience with MCP / Agentic AI architectures.

- Experience with AI data governance.

- Experience with ML/AI platforms and MLOps.

- Terraform / Infrastructure as Code.

- FinOps / cloud cost optimization.

Key Responsibilities

1. Data Architecture & Strategy

- Define enterprise and solution-level data architecture strategies and roadmaps.

- Design scalable architectures across Data Warehouse, Data Lake, Lakehouse, Data Fabric and Data Mesh patterns.

- Evaluate build-vs-buy, technology and platform choices based on business, scalability,



cost and performance requirements.

- Define architecture standards, principles and reusable patterns.

2. Modern Data Platforms

- Design cloud-native data platforms across AWS, Azure and/or GCP.

- Architect modern lakehouse solutions using technologies such as:

oDatabricks oSnowflake oMicrosoft Fabric oBigQuery oDelta Lake / Apache Iceberg

- Design batch, near-real-time and real-time data processing architectures.

3. Data Engineering & Integration

- Define architecture for:

oETL/ELT oAPI-based integration oStreaming pipelines oEvent-driven architectures oData ingestion and transformation
- Work with Data Engineers to establish scalable and reusable pipeline patterns.

- Define integration strategies across enterprise applications and data sources.

4. Data Modeling

- Design:

oConceptual, logical and physical data models o3NF models oDimensional models oStar/Snowflake schemas oData Vault where applicable
- Define enterprise data models, master data and business semantics.

- Establish standards for structured and unstructured data.

5. Data Governance & Security

- Define enterprise data governance frameworks covering:

oData ownership oData quality oMetadata oData lineage oData classification oRetention oAccess control
- Work with platforms such as Microsoft Purview, Unity Catalog, Collibra, Alation or equivalent.

- Ensure compliance, privacy and security requirements are incorporated into architecture.

AI / GenAI Data Architecture

This should be a key differentiator for the modern Data Architect profile.

6. AI-Ready Data Architecture

- Design data architectures that support Generative AI, RAG, AI copilots and Agentic AI.

- Define how structured, unstructured and semi-structured enterprise data can be made available to AI systems.

- Design data pipelines for AI ingestion, preprocessing, metadata and retrieval.

- Define architecture for enterprise knowledge bases and AI-ready data products.

Modern AI systems increasingly require more than a basic vector database; production RAG architectures need ingestion, metadata, retrieval,



evaluation, governance and access controls.

7. RAG & Retrieval Architecture

- Design enterprise RAG architectures using:

oEmbeddings oVector databases oHybrid search oMetadata filtering oSemantic search oRe-ranking
- Evaluate technologies such as Pinecone, Azure AI Search, OpenSearch, PostgreSQL/pgvector, Databricks Vector Search or equivalent.

- Define strategies for document ingestion, chunking, indexing, retrieval and data freshness.

8. Agentic AI Data Foundation

- Design data access patterns for AI agents and multi-agent systems.

- Define how agents securely access enterprise data, APIs and knowledge repositories.

- Design structured and unstructured data interfaces suitable for agent consumption.

- Understand concepts such as MCP, tool calling, agent memory and knowledge retrieval.

The shift toward AI-native lakehouse architectures is specifically driving requirements for continuous data, multimodal data and reliable context for AI agents.

Data Quality & Observability

- Establish enterprise data quality frameworks and standards.

- Define quality rules, validation frameworks and data contracts.

- Implement monitoring for:

oData freshness oCompleteness oAccuracy oConsistency oPipeline failures
- Define observability and lineage requirements across the data platform.

Performance & Cost Optimization

- Design architectures for high-volume and high-throughput workloads.

- Optimize storage, compute and data processing costs.

- Define partitioning, caching, indexing and query optimization strategies.

- Evaluate cloud/data platform cost-performance trade-offs.

- Establish architecture-level scalability and performance benchmarks.

AI Governance & Responsible Data Use

- Ensure AI solutions follow enterprise data governance and security requirements.

- Define access controls for data used by LLMs, RAG systems and AI agents.

- Address:

oPII / sensitive data oData leakage oAuthorization oData provenance oAuditability oRetention
- Ensure AI systems access only the data users are authorized to access.

This is increasingly important because AI/RAG governance needs to enforce authorization and data controls at runtime, not simply at the catalog level.

Job Snapshot Updated Date 17-08-2026 Job ID Harb723 Department Resource Management Group Location Baner, Pune, Maharashtra, India Experience 10 - 14 Years Employee Type Consultant

📌 Consultant-Data Architect / Modern Data & AI Architecture (Baner)
🏢 Harbinger Group
📍 Baner