Senior Architect

Title :
Senior Architect - Databricks (DE & Gen-AI)   Location   :
Chennai / Bangalore/ Hyderabad

Who we are

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value  are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.
Our team of 5000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.
We are a Great Place to Work-Certified™ (2022-25), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine.

Role Summary
We are looking for a highly experienced   Senior Architect   to lead the design, architecture, and implementation of enterprise-scale AI-native data platforms on the Databricks Data Intelligence Platform. This role requires a strategic technology leader with deep hands-on expertise in modern Data Engineering, Databricks, and Generative AI. The ideal candidate should be passionate about emerging technologies, capable of rapidly building prototypes and accelerators, mentoring engineering teams, and driving enterprise AI and data transformation initiatives.

Key Responsibilities
Lead the architecture, design, and implementation of enterprise-scale data and AI solutions on the Databricks Data Intelligence Platform.
Architect modern Lakehouse solutions supporting Data Engineering, AI/BI, Analytics, and GenAI workloads.
Design scalable, cloud-native data platforms with strong focus on Data Engineering, Data Modeling, Data Quality, Governance, Metadata Management, Security, and Platform Operations.
Build rapid prototypes, Proof of Concepts (PoCs), Proof of Value (PoVs), and reusable accelerators using Databricks and Generative AI capabilities.
Design and implement enterprise AI applications leveraging Databricks AI capabilities, Agentic AI, and intelligent analytics.
Partner with business stakeholders and clients to define technology roadmaps and translate business objectives into scalable technical solutions.
Collaborate with sales, pre-sales, and account leadership teams to support solutioning, proposals, client presentations, and business development initiatives.

Required Skills & Experience
Databricks & Data Engineering
Deep hands-on expertise with the   Databricks Data Intelligence Platform   and modern Lakehouse architecture.
Strong understanding of Databricks capabilities across Data Engineering, AI/BI, Governance, and AI application development.
Expert-level Python, PySpark, and SQL with experience building scalable, production-grade applications.
Strong expertise in:
Data Pipeline Design & Automation
Data Modeling & Lakehouse Architecture
Data Quality & Observability
Metadata Management & Data Governance
Streaming & Batch Data Processing
Performance Optimization
Generative AI & AI Engineering
Strong hands-on experience building enterprise AI applications on the Databricks Data Intelligence Platform.
Deep expertise in Databricks AI/BI, including Genie, conversational analytics, and AI-powered business intelligence solutions.
Hands-on experience designing and implementing Agentic AI solutions using Agent Bricks and the Databricks AI ecosystem.
Experience building semantic search and enterprise knowledge applications using Databricks Vector Search, enterprise metadata, and governed data assets.
Strong understanding of the Databricks AI application stack, including Mosaic AI, Agentbricks, Genie Agents, Databricks Apps.
Knowledge of Retrieval-Augmented Generation (RAG), AI Governance, Evaluation, Prompt Engineering, AI Observability, and…