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Data Architect (Product) – Manager

Grant Thornton INDUS

Bengaluru, Karnataka, India

Summary:

Grant Thornton GTech’s vision is centered around three strategic pillars: Application Modernization, Artificial Intelligence (AI), and Data. As GTech accelerates its product‑led transformation, data plays a foundational role in enabling scalable platforms, AI‑powered capabilities, and differentiated digital products. Our data strategy is focused on connecting, curating, and operationalizing enterprise data to drive measurable business outcomes, accelerate product delivery, and fuel innovation across service lines.

The GTech Data & Analytics function partners closely with Product, Data Engineering, and Enterprise Architecture, to design and deliver modern data platforms and capabilities that are product‑ready, reusable, and built for scale. The Data Architect (Product) is responsible for shaping and enabling the data architecture that underpins product development - from rapid pilots and proofs of concept through durable, enterprise‑grade platforms. This role is critical to ensuring data is modeled, integrated, governed, and operationalized in ways that support product roadmaps, iterative delivery, and AI‑enabled use cases while maintaining alignment with enterprise standards.

Working closely with Product Managers, Product Engineering teams, Enterprise Architects, and Data Engineering, the Data Architect (Product) designs data models, pipelines, and platform components that are reusable across products and service lines. This architect balances speed and innovation with data quality, security, and scalability, enabling Product teams to deliver high‑impact capabilities efficiently while ensuring data assets remain trusted, accessible, and aligned to GTech’s broader data and AI strategy.

Responsibilities

Partner with Product, Data & Analytics, and Enterprise Architecture to interpret and execute data architecture strategies that support product roadmaps, pilots, and scalable platform capabilities.
Design and evolve data architectures that underpin product platforms and AI‑enabled capabilities, including databases, data warehouses, lakes, and analytical data stores, ensuring alignment with product outcomes and enterprise standards.
Define data models, schemas, integration patterns, and platform components that are reusable across multiple products and service lines.
Design and enable data ingestion, transformation, and integration patterns (e.g., ELT/ETL, streaming, APIs) that support rapid product iteration while remaining production‑ready and secure.
Translate product and platform requirements into logical and physical data models, metadata, data quality rules, and governance controls that support analytics and AI use cases.
Work closely with Product Managers and Engineering leads to ensure data architecture decisions align with delivery timelines, release planning, and longer‑term product roadmaps.
Enable the creation of reusable data services, platform capabilities, and cost‑efficient patterns that accelerate product delivery and reduce duplication across teams.
Participate in architecture reviews, design forums, and governance checkpoints to balance speed with compliance, security, and architectural integrity.
Define, document, and enforce data standards, best practices, and guardrails that enable autonomy while ensuring consistency and trust in data assets.
Collaborate with data engineers, analytics teams, and downstream consumers to understand usage patterns and ensure data products are discoverable, accessible, and fit for purpose.
Evaluate and recommend modern data and analytics technologies to support evolving product, AI, and platform needs.
Contribute to solution architecture documentation and SDLC artifacts, ensuring traceability from product requirements through implementation.
Support the adoption of DataOps and platform‑oriented practices to improve automation, reliability, observability, and collaboration across product and data teams.
Monitor and optimize cloud and data platform usage, ensuring architectural decisions support cost efficiency, scalability, and long‑term sustainability.

Skills

Strong experience with Snowflake and Snowflake Cortex, including designing architectures that support analytics, AI/ML workloads, and emerging generative AI use cases.
Experience with Microsoft Azure data and AI services, such as Azure Data Factory, MS Fabric, Azure SQL, Azure ML Studio, and Azure AI Foundry.
Experience with Informatica IICS (CDI/CAI), Master Data Management, Power BI, and modern data integration and orchestration tools.
Experience modernizing and re‑platforming legacy data and analytics solutions into cloud‑native, product‑oriented architectures.
Demonstrated ability to lead or contribute to product pilots and proofs of concept, rapidly validating data and AI approaches and evolving them into scalable, production‑ready solutions.
Solid understanding of conceptual, logical, and physical data modeling; metadata management; lineage; and data quality techniques that support analytics and AI trust.
Ability to assess and recommend traditional and modern data architecture components based on product needs, delivery velocity, and long‑term platform strategy.
Strong interest in and practical exposure to AI, ML, and generative AI patterns, including their data architecture implications.
Experience working closely with Product Managers, engineering teams, architects, data teams, vendors, and service providers in cross‑functional delivery environments.
Familiarity with SDLC, agile, and product‑based delivery models, including solution‑level architecture and documentation practices.
Strong organizational, communication, and presentation skills, with experience explaining complex data and AI architectures to both technical and non‑technical audiences

Education / Professional Experience/ Qualifications

Bachelor of Science (BS) in Computer Science, Information Systems, or a related field (or equivalent practical experience).
10+ years of experience across enterprise IT and product engineering environments, with deep focus on modern data platforms, analytics, and application integration.
Proven hands‑on experience designing data architectures that support product platforms and AI‑enabled capabilities, including BI, ELT/ETL, APIs, microservices, and event‑driven integration patterns.
7+ years of experience in data modeling, database technologies, and solution‑level data architecture in cloud or hybrid environments.
Experience with professional services firms.Experience with Product development

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