Data Management Solution Architect – Data & AI

Location: Noida / Chennai / Bangalore/ Hyderabad/ Pune

Experience: 10+ years

Position Overview

We are looking for a highly skilled and customer-focused Data Management Solution Architect to support Data & AI solutioning initiatives for enterprise customers. The role will be responsible for creating differentiated, scalable, and business-aligned solution responses for RFPs, RFIs, RFQs, proactive proposals, client workshops, transformation engagements, and strategic deal pursuits.

The ideal candidate should have strong experience in enterprise data management, data governance, master data management, metadata management, data cataloging, data quality, data privacy, data stewardship, regulatory compliance, data maturity assessment, and cloud-enabled data management platforms. The role requires the ability to translate client business requirements into compelling technical solutions, architecture narratives, operating models, implementation roadmaps, effort estimates, and proposal-ready content.

This is an individual contributor role requiring strong solutioning, consulting, technical architecture, and stakeholder management capabilities across modern data management ecosystems on AWS, Microsoft Azure, and Google Cloud Platform.

Role Purpose

The Data Management Solution Architect will work closely with sales, presales, delivery, consulting, technology partners, and client stakeholders to define end-to-end enterprise data management solutions. The role will support business growth by shaping high-quality proposal responses, solution blueprints, operating models, governance frameworks, architecture diagrams, delivery approaches, reusable solution assets, and transformation roadmaps aligned to client objectives.

Key Responsibilities

• Lead and contribute to end-to-end presales solutioning activities for Data Management, Data Governance, Data Quality, Metadata Management, Data Catalog, Master Data Management, and Data Privacy opportunities.
• Analyze client RFPs, RFIs, RFQs, business requirements, current-state data landscape, governance maturity, pain points, compliance needs, and evaluation criteria to design fit-for-purpose data management solutions.
• Create solution responses covering technical approach, target-state architecture, governance operating model, stewardship model, platform design, implementation roadmap, assumptions, dependencies, risks, and differentiators.
• Architect enterprise data management solutions covering data governance, data quality, metadata management, business glossary, data cataloging, data lineage, data classification, data privacy, sensitive data management, reference data management, and master data management.
• Develop proposal-ready architecture narratives, operating model views, governance process flows, stewardship workflows, logical architecture diagrams, data management capability maps, and technology stack recommendations.
• Support solution estimation by defining work breakdown structures, delivery phases, team composition, assumptions, dependencies, accelerators, and high-level effort inputs.
• Participate in client workshops, discovery sessions, maturity assessments, solution walkthroughs, technical presentations, and proposal defense discussions.
• Stay updated on emerging trends in enterprise data management, active metadata, data fabric, data mesh governance, AI-enabled data governance, data observability, privacy engineering, Responsible AI governance, and AI-ready data foundations.

Principal KPIs for the Role

• Contribution to strategic deal wins
• Quality and timeliness of RFP/RFI responses
• Customer solution acceptance and feedback
• Reusable solution assets/frameworks contributed
• Innovation and value-added solution recommendations
• Effectiveness of proposed governance, data quality, privacy, and MDM solution approaches.

Core Competencies & Technical Expertise

• Strong expertise in Data Management and Data & AI solutioning for enterprise customers.
• Hands-on experience in preparing solution responses, architecture documents, presentations, technical proposals, operating models, and implementation roadmaps.
• Experience responding to RFPs/RFIs/RFQs and creating solution narratives, architecture diagrams, delivery approaches, governance models, and estimation inputs.
• Strong understanding of modern Data Management ecosystems including Data Governance, Master Data Management, Metadata Management, Data Catalog, Data Quality, Data Privacy, Data Lineage, Data Classification, Data Stewardship, Data Maturity Assessment, and Data Management Operations.
• Experience with cloud platforms such as Microsoft Azure, AWS, and Google Cloud Platform, especially in the context of cloud data governance, metadata management, data privacy, and data quality.
• Strong understanding of cloud-native and partner data management services across:
• Azure: Microsoft Purview, Azure Data Lake, Azure Synapse, Azure Databricks, Microsoft Fabric, Azure Data Factory,
• AWS: AWS Glue Data Catalog, AWS Lake Formation, Amazon S3, Amazon Redshift, AWS DMS, AWS data governance and security services.
• GCP: Dataplex, Data Catalog, BigQuery, Cloud Storage, Dataflow, Dataproc, GCP data governance patterns.
• Experience with leading Data Governance, Catalog, Metadata, Quality, MDM, and Privacy platforms such as: Collibra, Alation, Microsoft Purview, Informatica EDC / Axon / IDQ / MDM, IBM InfoSphere / IBM IGC / IBM Guardium, Stibo Systems, Reltio, TIBCO
• Strong knowledge of data governance operating models including governance council, data ownership, data stewardship, policy management, issue management, data quality rule management, and compliance reporting.
• Understanding of MDM concepts such as golden record, survivorship rules, match-merge, hierarchy management, reference data management, stewardship workflows, data standards, and 360-degree entity views.
• Strong understanding of data quality frameworks including profiling, rule definition, monitoring, scorecards, exception management, remediation workflows, and self-healing data quality patterns.
• Knowledge of metadata management concepts including business glossary, technical metadata, operational metadata, lineage, catalog onboarding, automated metadata ingestion, metadata curation, search, discovery, and knowledge graph-based metadata models.
• Understanding of data privacy and sensitive data management including data classification, masking, tokenization, encryption, access controls, regulatory compliance, and privacy-by-design principles.
• Familiarity with modern AI technologies including LLMs, AI-assisted governance, intelligent metadata discovery, automated data classification, GenAI-enabled business glossary creation, and Responsible AI governance patterns.
• Strong communication and presentation skills with experience interacting with customer stakeholders, CDO organizations, data governance councils, business data owners, technology leaders, and compliance stakeholders.
• Ability to work independently across multiple concurrent opportunities in a fast-paced presales environment.

Required Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related field.
• DAMA CDMP certification is strongly preferred for Data Management Architect roles. Certifications or formal training in Collibra, Alation, Informatica, Microsoft Purview, MDM platforms, data privacy, cloud data governance, and hyperscaler data services will be an added advantage.
• Strong hands-on and architectural understanding of enterprise data management platforms, governance frameworks, MDM platforms, data cataloging, data quality, metadata management, and data privacy solutions.
• Strong understanding of cloud data management services across AWS, Azure, and GCP.
• Experience in presales solutioning, proposal development, consulting, or customer-facing architecture roles is strongly preferred.
• Minimum 10 years of experience in Data Management, Data Governance, MDM, Data Quality, Metadata Management, Data Privacy, or Data & AI technologies, with relevant experience in solution architecture and presales solutioning.
• Experience in customer-facing engagements, workshops, data maturity assessments, proposal discussions, and solution presentations will be an added advantage.