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 as Event Hubs, Kafka, or Fabric Real-Time Intelligence.
• Certifications such as Azure Solutions Architect Expert, Fabric Analytics Engineer (DP-600), Azure Data Engineer (DP-203), or Databricks Data Engineer Professional.
• Experience with DevOps/DataOps, CI/CD for data pipelines, and Infrastructure as Code (Terraform, Bicep).

Ideal Candidate
Someone who can sit with customer leadership, understand the business and AI use cases, assess what the data foundation is missing, design the target architecture and roadmap, and then guide the team (and step in hands-on when needed) to build the platform, pipelines, models, and AI-ready datasets.