Data Architect Enterprise Data AI Foundations

Position: - Data Architect Enterprise Data & AI Foundations

Hybrid to Hyderbad

Role Summary
Tachyon Technologies is looking for a hands-on Data Architect to design and lead enterprise data foundations for AI, analytics, automation, GenAI, and reporting use cases, mainly for Microsoft/Azure-based customers.

This role works with customers, business leaders, AI teams, and engineering teams. The Data Architect will assess the current data landscape, define the target-state architecture and roadmap, and set the standards for data lakes, warehouses, lakehouses, and AI-ready data layers. The person will own the architecture decisions and will also work hands-on during design, proofs of concept, and critical build phases.

Key Responsibilities

• Work with customer business and technology stakeholders to understand business processes, AI use cases, analytics needs, and reporting requirements.
• Assess current data sources, platforms, data quality, integration gaps, and AI/analytics readiness, and present findings with recommendations.
• Define the target-state data architecture and a phased data foundation roadmap for AI and analytics programs.
• Design enterprise data platforms on Azure and Microsoft Fabric, including data lakes, data warehouses, lakehouses, Medallion (Bronze/Silver/Gold) layers, and curated data products.
• Define conceptual, logical, and physical data models, including dimensional models, canonical models, and semantic layers for Power BI.
• Set integration patterns for batch, near real-time, CDC, API-based, and event-driven ingestion from ERP, CRM, databases, files, APIs, and operational systems.
• Design data architecture for GenAI use cases, including RAG pipelines, document ingestion, chunking and embedding strategy, vector stores, and grounding data for copilots and AI agents.
• Define standards for data security, access control, privacy, metadata, cataloging, lineage, and governance, using tools such as Microsoft Purview and Unity Catalog.
• Set frameworks for data quality, reconciliation, validation, error handling, observability, and pipeline monitoring.
• Make platform and tool choices (Fabric vs. Databricks vs. Synapse, storage formats, compute sizing) and keep cost, performance, and scalability in balance.
• Build reference architectures, reusable templates, and accelerators, and contribute hands-on to POCs and complex pipeline or model development using SQL, Python, and PySpark.
• Review designs and code from data engineers and guide the engineering team on architecture standards and best practices.
• Lead pre-sales architecture work, including customer discovery, solution design, RFP responses, effort estimation, and proposal content for Data + AI programs.
• Present architecture, implementation approach, risks, and roadmap clearly to customer executives and internal leadership.

Required Skills

• 12+ years of experience in data engineering, data platforms, or data architecture, with at least 4 years in an architect role.
• Proven experience designing and delivering enterprise data lakes, data warehouses, or lakehouse platforms end to end.
• Deep expertise in the Microsoft data ecosystem: Azure Data Factory, ADLS Gen2, Synapse, Microsoft Fabric (OneLake, Lakehouse, Warehouse), Databricks, Azure SQL, and Power BI.
• Strong data modeling skills, including dimensional modeling (Kimball), Data Vault, and semantic model design.
• Strong SQL plus working hands-on skills in Python and PySpark.
• Experience defining data governance, security, metadata, and lineage frameworks.
• Experience with integration patterns across APIs, databases, files, and enterprise applications.
• Ability to translate business and AI use cases into architecture, data requirements, and delivery roadmaps.
• Experience leading data engineering teams and setting technical direction across projects.
• Excellent communication, documentation, and executive stakeholder management skills.

Good to Have

• Hands-on architecture experience with GenAI, RAG, vector databases (Azure AI Search, Cosmos DB vector, pgvector), semantic search, copilots, or agentic AI.
• Experience with Microsoft Purview, Unity Catalog, or other enterprise catalog and governance tools.
• Domain or source-system experience with SAP, Salesforce, ServiceNow, Dynamics 365, or finance, operations, and customer data.
• Knowledge of data privacy and compliance needs (GDPR, HIPAA, SOC 2, CCPA).
• Experience with streaming tools such a…