GDT Data Solution Architect

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GDT Data Solution Architect

Data Science & Analytics

Job Description

Job Title: GDT Data Solution Architect

Location: Bangalore, India

ABOUT UNILEVER:

Be part of the world’s most successful, purpose-led business. Work with brands that are well-loved around the world, that improve the lives of our consumers and the communities around us. We promote innovation, big and small, to make our business win and grow; and we believe in business as a force for good. Unleash your curiosity, challenge ideas and disrupt processes; use your energy to make this happen. Our brilliant business leaders and colleagues provide mentorship and inspiration, so you can be at your best. Every day, nine out of ten Indian households use our products to feel good, look good and get more out of life – giving us a unique opportunity to build a brighter future.

Background:

For Unilever to remain competitive in the future, the business needs to continue the path to becoming data intelligent. The Global Digital & Technology team will empower Unilever’s journey to becoming an Intelligent Enterprise - powering key decisions with data, insights, advanced analytics, and AI. Our ambition is to enable democratization of data, information and insights as a completely agile organization that builds fantastic careers for our people and is accountable for delivering great work that maximizes impact and delivers growth.

We would be accountable for impact of solutions, maintaining market relevance and driving P&L, customer, and consumer value from analytics products. You’ll collaborate with global product and technical teams, business stakeholders, and cross-functional partners to ensure successful project execution.

Role Overview:

We are seeking a Data Solution Architect to drive the end-to-end design and development of data and analytics products within the Data Foundation ecosystem. This role spans across data platforms, BI, data lakes, data warehousing, web applications, ETL pipelines, and AI-driven solutions, ensuring that all solutions are scalable, governed, and AI-ready.

As a key technical leader, you will be responsible for architecting integrated data solutions, enabling intelligent decision-making, and preparing enterprise data assets for advanced analytics and AI consumption. This role requires strong expertise across Azure and GCP ecosystems, along with a deep understanding of modern data and AI architecture patterns, including agentic and generative AI solutions.

Key Responsibilities

End-to-End Solution Architecture

• Design and own holistic data and analytics architectures covering:

  • Data ingestion, processing, and transformation (ETL/ELT)

  • Data lakes, data warehouses, and semantic layers

  • BI and reporting solutions (Power BI, dashboards, datasets)

  • Web applications 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, supporting enterprise-scale use cases.

Data Platform & Engineering Excellence

• 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.

AI & Advanced Analytics Enablement

• 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 and multi-agent workflows

  • RAG-based and conversational analytics solutions

• Apply best practices for AI governance, accuracy, monitoring, and responsible AI adoption.

Cloud & Cross-Platform Architecture

• Build and guide solutions across Azure and Google Cloud (GCP) ecosystems.

• Define integration strategies across Databricks, cloud-native services, and third-party tools.

• Ensure solutions align with enterprise cloud strategy, security standards, and platform governance.

Business & Product Alignment

• Partner with business stakeholders to shape data products and analytics use cases.

• Translate business requirements into scalable technical designs and deliverable architectures.

• Drive end-to-end ownership from concept to delivery and adoption, ensuring measurable business impact.

Leadership & Governance

• Act as a technical authority and mentor, guiding engineering teams and ensuring alignment to best practices.

• Lead architecture reviews, enforce data governance, security, and compliance standards.

• Document architecture patterns, design decisions, and reusable frameworks for enterprise adoption.

Required Skills & Experience

Core Technical Expertise (Strong Hands-on)

• Strong hands-on experience in Python with ability to build production-grade data and AI solutions.

• Deep expertise in 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.

Secondary / Cross-Platform Expertise

• Good hands-on experience and working knowledge of:

  • Azure ecosystem (ADF, Azure SQL, MS SQL Server)

  • Data integration and pipeline design using ADF or equivalent tools

• Understanding of multi-cloud architecture patterns (Azure + GCP) and interoperability across platforms.

Application & Product Development

• Experience in web application architecture design, including API-first and microservices-based patterns.

• Hands-on experience in at least one modern web/backend stack:

  • Node.js / React

  • .NET

  • Python (FastAPI or similar frameworks)

• Ability to design and build data-driven applications and scalable backend services.

AI, Data & Agentic Architecture

• 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.

Software Engineering & Delivery

• Strong foundation in software engineering, with experience designing and delivering production-grade systems.

• Proven ability to:

  • Translate architecture into working code

  • Build scalable, modular, and maintainable solutions

• Experience with microservices and distributed system design patterns.

Preferred Experience

• Experience in building data and analytics products across:

  • Data lakes / Lakehouse architectures

  • Data warehousing

  • 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).

Cultural Fit

• Comfortable working in a fast-paced environment with evolving requirements.

• Collaborative mindset with a focus on solving real business problems.

Good to Have / Preferred Skills

• 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 AzureDevOps / 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

Note: All official offers from Unilever are issued only via our Applicant Tracking System (ATS). Offers from individuals or unofficial sources may be fraudulent—please verify before proceeding.

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