Lead Technical Architect – Data Platform Modernization

Tata Consultancy Services; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Tata Consultancy Services in Bengaluru seeks a Lead Technical Architect to drive enterprise-scale data platform modernization, migrating from legacy warehouses to Databricks Lakehouse and dbt-based architectures. You will define target-state architectures, roadmaps, and govern modernization programs across cloud platforms.

You will mentor teams, engage with executives, and lead migration patterns, Delta Lake, Unity Catalog, and Lakehouse strategies, enabling analytics, AI/ML, and self-service BI

Qualifications

• 12–20 years of experience in data architecture and cloud data platforms.
• Experience leading enterprise-scale data modernization programs.
• Strong leadership to drive migration roadmaps and governance.

Responsibilities

• Define enterprise modernization strategy and target-state architecture.
• Lead migration from legacy ETL/data warehouses to Databricks Lakehouse with dbt.
• Mentor architects and engineers; engage executives with roadmaps and risk assessments.
• Oversee CI/CD, DataOps/MLOps practices and governance frameworks.

Skills

SQL

PySpark / Spark

Python

Data Modeling

ETL/ELT Architecture

Data Warehousing Concepts

API Integrations

Tools

Databricks

Delta Lake

Unity Catalog

dbt

Terraform

Azure DevOps

GitHub Actions

Job description

Lead Technical Architect - Data Platform Modernization (Databricks & dbt)

Location

Experience

12-20 Years

Role Summary

We are seeking an experienced Lead Technical Architect to lead enterprise-scale data platform modernization initiatives, including migration from legacy/on-premise data warehouses, ETL platforms, and cloud data ecosystems to Databricks Lakehouse and dbt-based modern data architectures.

The ideal candidate will possess deep expertise in cloud data platforms, data architecture, data engineering, migration strategies, governance, and modernization programs. This role requires strong leadership skills to define target-state architectures, drive migration roadmaps, mentor engineering teams, and engage with business and executive stakeholders.

Key Responsibilities

Architecture & Strategy

• Define enterprise data modernization strategy and target-state architecture leveraging Databricks Lakehouse and dbt.
• Design scalable, secure, and high-performance data platforms supporting analytics, AI/ML, reporting, and self-service BI.
• Develop migration blueprints for legacy ETL, data warehouses, and reporting platforms.
• Create reference architecture, standards, and governance frameworks

Data Platform Migration

• Lead migration initiatives from platforms such as:
• Assess source systems and define migration patterns, tooling, and execution strategies.
• Drive workload transformation to Databricks using Delta Lake, Unity Catalog, and Lakehouse architecture.
• Design and implementation:
• Delta Lake architecture
• Medallion Architecture (Bronze, Silver, Gold)
• Unity Catalog governance model
• Data Quality frameworks
• Performance optimization strategies
• Cost optimization and FinOps practices
• Establish best practices around Spark, PySpark, SQL, and Databricks Workflows.
• Define enterprise dbt standards and deployment strategies.
• Design modular, reusable dbt models and frameworks.
• Implement testing, lineage, documentation, and CI/CD pipelines.
• Establish best practices for semantic layers and data transformation governance.
• Architect solutions on Azure, AWS, or GCP.
• Implement Infrastructure as Code (Terraform preferred).
• Integrate CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or GitLab.
• Drive DataOps and MLOps practices across teams.

Leadership & Stakeholder Management

• Lead teams of architects, data engineers, and consultants.
• Conduct architecture reviews and technical governance forums.
• Engage business stakeholders to understand transformation goals.
• Provide executive-level presentations, estimates, risk assessments, and roadmaps.

Technical Skills

• SQL (Expert Level), PySpark / Spark, Python, Data Modeling (Dimensional & Data Vault) , ETL/ELT Architecture, Data Warehousing Concepts ,API Integrations