Tech SME - Databricks

Tiger Analytics; Chennai; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Tiger Analytics is seeking a seasoned data engineering leader to drive governance and architecture for large-scale Azure-based data platforms. You will guide data lakehouse/data mesh initiatives, lead design across data services, and ensure scalable, secure data access for BI and data science teams.

You will also shape technical roadmaps, manage risks, and mentor a large delivery organization while advancing GenAI capabilities and industry-leading analytics practices.

Qualifications

• 15+ years of overall data engineering experience.
• 5+ years in Big Data and Azure ecosystems.
• Deep hands-on and architectural mastery of Azure Databricks, PySpark, Unity Catalog, Delta Live Tables, Databricks Workflows, and SQL Warehouses.
• Expertise in Azure Data Factory, Microsoft Fabric, Event Hub, Cosmos DB, Unity Catalog, and Purview.
• Strong experience with Hadoop, Spark, Kafka, Hive, Impala, Sqoop, and NoSQL databases.
• Proficiency in Python or Scala/Java and CI/CD pipelines (Git/Jenkins/Azure DevOps).
• Solid data modeling concepts across Lakehouse and relational/NoSQL models.
• Active engagement with GenAI/LLMs to optimize delivery pipelines.

Responsibilities

• Act as Unit Technology Owner, driving governance, reviews, and execution across customer programs.
• Advise clients on engineering and architecture decisions for data lakehouse, data warehouse modernization, data fabric and data mesh.
• Lead architecture and solution design across hyperscalers, data services, and BI layers.
• Ensure data platforms support BI and Data Science teams with secure, scalable models.
• Partner with client architects and stakeholders, delivering thought leadership.
• Drive proposal development, solution design, roadmaps, and estimation methodologies.
• Translate complex problems into modular cloud-scale data platform solutions.
• Identify risks and mitigation strategies for technical execution.
• Lead GenAI initiatives to improve engineering efficiency and unlock business value.
• Mentor and grow data engineering teams (100–150 people) and drive talent acquisition.

Skills

Azure Databricks

PySpark

Delta Live Tables

Azure Data Factory

Microsoft Fabric

Cosmos DB

Hadoop/Spark

Python/Scala/Java

GenAI/LLMs

Tools

Git

Jenkins

Azure DevOps

SQL

Job description

Tiger Analytics is a global AI and analytics consulting firm. With data and technology at the core of our solutions, our tribe is solving problems that eventually impact the lives of millions globally. Our culture is modeled around expertise and respect with a team-first mindset. Headquartered in Silicon Valley, you’ll find our delivery centers across the globe and offices in multiple cities across India, the US, the UK, Canada, and Singapore, including a substantial remote global workforce. We’re Great Place to Work-Certified™. Working at Tiger Analytics, you’ll be at the heart of an AI revolution. You’ll work with teams that push the boundaries of what is possible and build solutions that energize and inspire. Curious about the role? What your typical day would look like?

Mandatory Skills

Role

Key Responsibilities: -

Technical Governance & Architectural Leadership

• Act as the Unit Technology Owner, driving end-to-end technology governance, deep technical reviews, and high-quality execution across all customer programs.
• Provide high-impact client advisory on engineering and architecture decisions spanning Data Lake/Lakehouse implementations, Data Warehouse Modernization, Data Fabric, and Data Mesh architectures.
• Lead architecture and solution design across hyperscalers, data services, and BI layers (excluding Application and pure Data Science layers).
• Ensure data platforms support BI and Data Science teams through secure, governed, and scalable data consumption models.
• Partner with client Tech Architects and senior enterprise stakeholders, delivering consultative and strategic thought leadership.

Pre-Sales, Solutioning & Growth

• Spearhead proposal development, solution design, technical roadmaps, and estimation methodologies for growth pursuits.
• Translate complex business problems into modular, cloud-scale data platform solutions.
• Define robust technical execution approaches, identify technical project risks, and formulate mitigation strategies.

Innovation, GenAI & Capability Building

• Drive the adoption of emerging AI technologies (including GenAI and LLMs) to improve engineering efficiency and unlock innovative business use cases.
• Track latest technological breakthroughs and develop Proofs of Concept (PoCs) and Points of View (PoVs) to enhance institutional capabilities.
• Identify proprietary intellectual property (IP) opportunities for Tiger Analytics and direct development efforts.
• Steer capability development initiatives through Tiger’s Innovation Lab and Learning & Development Academy.
• Lead, mentor, and grow multiple high-performing data engineering teams across a delivery scale of 100–150 people.
• Drive talent acquisition, technical hiring bars, and competency benchmarking across delivery centers.
• Represent Tiger Analytics at tech industry conferences, customer forums, and partner ecosystems.

Required Experience & Technical Capabilities

• Industry Experience: 15+ years of overall data engineering experience, including 5+ years dedicated to the Big Data and Azure ecosystems.
• Enterprise Delivery Track Record: Proven track record delivering large-scale data modernization, data foundation, and data mesh/data product initiatives at enterprise scale (e.g., global multi-brand enterprises such as Mars).
• Databricks Ecosystem (Must-Have): Deep hands-on and architectural mastery of Azure Databricks, PySpark, Notebooks, Unity Catalog, Delta Live Tables (DLT), Databricks Workflows, and SQL Warehouses.
• Azure Cloud Data Ecosystem: Expertise in Azure Data Factory (ADF), Microsoft Fabric, Azure Event Hub, Stream Analytics, Cosmos DB, Unity Catalog, and Microsoft Purview.
• Big Data & Distributed Systems: Hands-on background with Hadoop, Apache Spark, Kafka, Hive, Impala, Sqoop, and NoSQL databases (HBase, MongoDB, Cassandra).
• Engineering & CI/CD Practices: Proficiency in Python or Scala/Java, alongside automated CI/CD pipelines via Git, Jenkins, or Azure DevOps.
• Data Modeling: Solid command of data modeling concepts across modern Lakehouse and relational/NoSQL paradigms.
• AI & LLM Maturity: Active engagement and thought maturity in integrating GenAI and LLMs to optimize delivery pipelines and deliver business value.

Desirable / Preferred Qualifications

• Experience working across multi-cloud environments (AWS / GCP).
• Domain expertise in the Consumer Packaged Goods (CPG) / FMCG sector.
• Exposure to collaborative delivery models alongside Business Intelligence (BI) and advanced Data Science functions.