GDT Data Solution Architect

Overview

Lead the end-to-end design and development of data and analytics products within the Data Foundation ecosystem, spanning data platforms, BI, data lakes, data warehousing, web applications, ETL pipelines and AI-driven solutions.

What you'll do

• Design and own holistic data and analytics architectures covering data ingestion, processing and transformation (ETL/ELT), data lakes, data warehouses, semantic layers, BI and reporting solutions, and data-driven products.
• Define scalable, modular and reusable architecture patterns aligned to Data Foundation standards.
• Ensure solutions are cost-efficient, performant and future-ready for enterprise-scale use cases.
• Architect and optimise data pipelines and workflows for reliability, scalability and maintainability.
• Drive standardisation across UDL, BDL and product data layers, ensuring minimal redundancy and efficient data flow.
• Establish best practices for data modelling, partitioning, incremental processing and performance optimisation.
• Enable self-service data capabilities through governed, well-structured data models.
• Design solutions that make data AI-ready, including semantic modelling, metadata enrichment and knowledge layers.
• Lead the adoption of AI/ML and generative AI patterns, including agentic architectures, multi-agent workflows, RAG-based and conversational analytics solutions.
• Apply best practices for AI governance, accuracy, monitoring and responsible AI adoption.
• Build and guide solutions across Azure and Google Cloud (GCP) ecosystems, and define integration strategies across Databricks, cloud-native services and third-party tools.
• Ensure solutions align with enterprise cloud strategy, security standards and platform governance.
• Partner with business stakeholders to shape data products and analytics use cases, and translate business requirements into scalable technical designs.
• Drive end-to-end ownership from concept to delivery and adoption, ensuring measurable business impact.
• Act as a technical authority and mentor, guiding engineering teams and ensuring alignment to best practices.
• Lead architecture reviews and enforce data governance, security and compliance standards.
• Document architecture patterns, design decisions and reusable frameworks for enterprise adoption.

What you'll need

• Strong hands-on experience in Python with ability to build production-grade data and AI solutions.
• Deep expertise in the Databricks ecosystem, including Delta Lake, Unity Catalog and workflow orchestration.
• Advanced proficiency in SQL / PostgreSQL, with strong data modelling and optimisation skills.
• Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
• Experience with Google Cloud Platform (GCP) services, including Cloud Run and cloud-native architectures.
• Strong experience in Power BI development, including semantic modelling and performance optimisation.
• Proven expertise in GCP architecture and solution design, delivering scalable and enterprise-ready systems.
• Strong understanding of AI/ML solution design, including data preparation for AI consumption.
• Hands-on experience with agentic AI frameworks such as LangChain, AutoGen, CrewAI, or similar.
• Experience building agent-driven or conversational AI solutions (RAG, orchestration, workflow agents).
• Understanding of event-driven architectures, APIs and distributed systems.
• Knowledge of data security, governance and access control, especially in AI/agentic architectures.
• Strong foundation in software engineering, with experience designing and delivering production-grade systems.
• Proven ability to translate architecture into working code and build scalable, modular and maintainable solutions.
• Experience with microservices and distributed system design patterns.
• Experience in at least one modern web/backend stack: Node.js / React, .NET, or Python (FastAPI or similar).
• Good hands-on experience and working knowledge of the Azure ecosystem (ADF, Azure SQL, MS SQL Server).
• Experience with data integration and pipeline design using ADF or equivalent tools.
• Understanding of multi-cloud architecture patterns (Azure + GCP) and interoperability across platforms.
• Experience in web application architecture design, including API-first and microservices-based patterns.

Nice to have

• Experience building data and analytics products across data lakes / Lakehouse architectures, data warehousing, and BI and reporting platforms.
• Exposure to AI-driven or agentic solutions in enterprise environments.
• Experience working in data-intensive or digital-first organisations (e.g., media, analytics, or platform companies).
• Certification in GCP Architecture (e.g., Google Professional Cloud Architect).
• Additional certifications in Azure Data / AI / Architecture (e.g., Azure Solutions Architect, Azure Data Engineer).
• Exposure to Databricks certifications or advanced training in Lakehouse architecture.
• Experience with end-to-end AI product development lifecycle, including experimentation to productionisation.
• Familiarity with Azure DevOps / CI-CD practices for data and AI solutions.
• Knowledge of data governance frameworks and enterprise data cataloguing tools.
• Experience working in large-scale enterprise data transformation or cloud migration initiatives.
• Exposure to modern data stack tools and emerging AI platforms.

Details

• Location: Bangalore, India.