Head Of Solution Architecture – Data Management, Analytics & AI/ML

Edgematics; India; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Edgematics is seeking an Enterprise Data Executive to lead data transformation initiatives, align data architecture with business strategy, and drive high-value consulting engagements globally. The role emphasizes bridging complex data systems with C-suite objectives and delivering scalable data platforms across multiple regions.

The ideal candidate brings 20+ years in data management, analytics, and AI, plus deep experience in presales, architecture, and governance.

Qualifications

• 20+ years of experience in data management, analytics and AI.
• Proven ability to lead enterprise data architecture and presales.
• Experience with RFP/RFI responses and client engagements.

Responsibilities

• Lead technical presales engagements for Data Management, Analytics and AI/ML opportunities.
• Translate business objectives into technical requirements and solution capabilities.
• Develop clarification questions and assumptions for accurate solution design.
• Prepare comprehensive technical proposals and responses to RFPs/RFIs.
• Engage with CIO/CDO/CTO and enterprise data teams across geographies.

Skills

Presales

Solution Architecture

Technical Consulting

RFP/RFI responses

Stakeholder Engagement

Education

Bachelor's or Master's in CS/IT/Engineering/Data Science

Tools

Snowflake

Databricks

Data Lakehouse architectures

Job description

About Edgematics

Edgematics is a global data and AI consulting company empowering Fortune 500 enterprises across the Middle East, Europe, the UK, North America, and India to unlock the true value of their data. Through our advanced platforms — PurpleCube AI (Data Orchestration & Analytics) and Axoma (Agentic AI) — we help organizations accelerate data modernization, governance, and AI adoption at scale. Our mission is to transform raw data into actionable intelligence through world-class engineering, innovation, and deep domain expertise.

Job Description

Looking for A visionary and results-driven Enterprise Data Executive with over 15+ years of experience leading large-scale data transformation, modern data architecture, and high-impact consulting practices. This candidate excels at bridging the gap between complex technical data infrastructure and overarching corporate strategy, turning raw data assets into measurable business value, revenue streams, and competitive advantages. Expert in designing and deploying modern data ecosystems—including Data Lakes, Data Mesh architectures, cloud data warehouses (Snowflake, Databricks), and real-time analytics pipelines that scale seamlessly with business growth. A seasoned advisory leader who has successfully managed high-value consulting portfolios across the globe , built elite cross-functional data teams, and served as a trusted advisor to C-suite executives on digital transformation journeys. A collaborative leader known for breaking down organizational silos and fostering a high-performance culture. This candidate possesses the unique ability to articulate deeply complex data concepts into clear, strategic narratives for non-technical stakeholders, securing institutional buy-in and driving adoption at all levels. An expert in cultivating cross-border talent, seamlessly orchestrating diverse teams of architects, data engineers, and consultants globally. Proven track record of scaling and managing high-performing, geographically distributed data teams across North America, EMEA and APAC.

Presales & Customer Engagement

• Lead technical presales engagements for Data Management, Analytics and AI/ML opportunities.
• Participate in customer discovery sessions, requirement workshops and technical discussions.
• Understand business objectives and translate them into technical requirements and solution capabilities.
• Identify gaps, assumptions, dependencies, risks and clarification points during opportunity qualification.
• Conduct technical presentations, solution walkthroughs and architecture discussions with customer stakeholders.
• Engage effectively with CIO/CDO/CTO organizations, enterprise architects, data teams and business stakeholders.
• Support sales teams throughout the opportunity lifecycle from qualification through proposal, technical evaluation and closure.

RFP / RFI / Proposal Management

• Analyse complex RFPs, RFIs, RFQs and Statements of Work.
• Extract functional, technical, infrastructure, security, integration, governance and non-functional requirements.
• Develop clarification questions and assumptions required for accurate solution design.
• Prepare comprehensive technical proposals and responses.
• Develop compliance matrices and requirement-to-solution mappings.
• Define solution scope, exclusions, assumptions, dependencies and customer responsibilities.
• Ensure technical proposals are aligned with customer requirements and evaluation criteria.
• Participate in technical defense sessions and proposal presentations.

Solution Architecture

Design end-to-end enterprise architectures covering

• Data Integration and ETL/ELT
• Batch and real-time data ingestion
• CDC and data replication
• Data Warehouses and Data Lakes
• Modern Data Lakehouse architectures
• Data Quality and Data Observability
• Master Data Management
• Metadata Management and Data Catalog
• Data Governance & Data Lineage
• API and application integration
• Streaming and event-driven architectures
• BI, Reporting and Self-Service Analytics
• Semantic Layers
• AI/ML and Generative AI platforms
• Enterprise Agentic AI architectures

Develop architecture artifacts including

• Conceptual Architecture
• Logical Architecture
• Physical / Deployment Architecture
• Data Flow Architecture
• Integration Architecture
• Network Architecture
• Security Architecture
• HA/DR Architecture
• DevSecOps / CI/CD Architecture
• Data Governance Architecture
• AI/ML and GenAI Architecture

Technology Evaluation & Solution Selection

• Evaluate and recommend technologies based on customer requirements, existing technology landscape, scalability, security, performance, cost and operational considerations.
• Expected exposure to technologies such as:
  • Data Integration & Management
  • Data Platforms
  • Big Data & Streaming
  • Data Governance
  • Analytics
• The candidate should be technology-agnostic and capable of selecting an appropriate technology stack rather than forcing a predefined product.

AI/ML, Generative AI & Agentic AI

• Support presales opportunities involving AI/ML and Generative AI.
• Design high-level enterprise AI/ML architectures covering data preparation, model development, deployment and monitoring.
• Understand LLM-based solution architectures, RAG, vector databases and enterprise knowledge architectures.
• Understand AI Agents and Agentic AI architectures.
• Define integration patterns between enterprise applications, data platforms, LLMs and AI services.
• Understand AI governance, model governance, responsible AI and security requirements.
• Define AI guardrails, model evaluation, observability and human-in-the-loop controls.
• Identify suitable AI/ML and GenAI use cases based on customer business requirements.
• Support AI/ML PoCs, prototypes and technical demonstrations.

Sizing, Estimation & Commercial Support

• Prepare implementation effort estimates and resource plans.
• Develop project timelines, work breakdown structures and delivery phases.
• Estimate person-days/person-months across project roles.
• Define resource loading and team composition.
• Develop phased implementation approaches including Discovery, PoC/Pilot, Implementation, UAT, Go-Live, Hypercare and Support.
• Support infrastructure sizing and Bill of Quantity (BoQ) preparation.
• Work with OEMs/technology partners for software licensing and infrastructure sizing.
• Support commercial teams in validating solution assumptions that influence pricing.

Infrastructure, Cloud & Non-Functional Design

• Understand on-premises, cloud and hybrid deployment architectures.
• Define infrastructure requirements for DEV/UAT/PROD and DR environments.
• Contribute to compute, storage and network sizing.
• Define HA/DR approaches including RPO/RTO considerations.
• Understand enterprise networking concepts including firewalls, load balancers, VPNs, proxies and DMZ architectures.
• Address security, encryption, IAM, RBAC, secrets management and audit requirements.
• Define monitoring, logging, backup and recovery requirements.

PoC & Technical Validation

• Define PoC objectives, scope and success criteria.
• Work with engineering teams to develop demonstrations and prototypes.
• Validate proposed technologies against customer use cases.
• Demonstrate technical feasibility and solution value.
• Document PoC findings and translate successful PoCs into production solution architectures.

Collaboration & Leadership

• Coordinate with Sales, Delivery, Engineering, Product/OEM teams and SMEs.
• Provide technical leadership during strategic opportunities.
• Review architectures and proposals developed by other team members.
• Mentor junior presales consultants and solution architects.
• Maintain reusable solution assets, reference architectures, estimation models and proposal templates.
• Conduct internal technical reviews before customer submission.

Required Experience

• 20+ years of overall experience in Data Management, Data Engineering, Analytics, AI/ML or related technology areas.
• Strong experience in Presales, Solution Architecture & Technical Consulting.
• Proven experience handling enterprise RFP/RFI responses.
• Strong understanding of modern enterprise data architectures.
• Experience designing both on-premises and cloud-based solutions.
• Experience with Data Lake, Data Warehouse and Lakehouse architectures.
• Strong understanding of ETL/ELT, CDC, Query Engines, Storage technologies, Table Formats, APIs and streaming integration.
• Understanding of Data Governance, Data Quality, Metadata, Lineage and MDM.
• Exposure to AI/ML, Generative AI and modern LLM-based solution architectures.
• Experience developing technical proposals, architecture diagrams, project estimates and resource plans.
• Experience interacting directly with enterprise customers and senior stakeholders.

Preferred Skills

• Enterprise architecture and solution-design methodologies.
• Cloud architecture experience across Azure, AWS and/or GCP.
• Microsoft Fabric / Databricks / Snowflake / Cloudera exposure/Cloud Pack for Data.
• Informatica, Data Stage, Qlik Talend & CDC ecosystem experience.
• Kafka, Spark, Airflow and modern data engineering frameworks.
• Data Governance and MDM platforms.
• GenAI, RAG, LLM, Vector DB and Agentic AI concepts.
• DevOps/DevSecOps and CI/CD for data platforms.
• Kubernetes/OpenShift/container-based platforms.
• Security and HA/DR architecture.

Key Competencies

• Solution thinking: Ability to convert ambiguous business requirements into practical technical solutions.
• Architecture capability: Ability to design complete end-to-end architectures rather than individual technology components.
• Presales mindset: Ability to balance technical excellence, customer requirements, delivery feasibility and commercial viability.
• Communication: Ability to explain complex architecture to both technical and executive stakeholders.
• Estimation: Ability to develop realistic implementation plans, resource estimates and sizing assumptions.
• Technology breadth: Strong understanding across data, analytics, cloud and AI technologies.
• Leadership: Ability to coordinate multiple SMEs and technical teams during complex pursuits.
• Proposal ownership: Ability to independently drive the technical portion of strategic bids from requirement analysis through final submission and technical defence.

Typical Deliverables

• RFP/RFI technical responses
• Technical proposals
• Solution architecture documents
• Architecture diagrams
• Technology-stack recommendations
• Compliance matrices
• Clarification questionnaires
• Scope and assumption documents
• Infrastructure sizing and BoQs
• Effort estimates
• Resource loading plans
• Project implementation plans
• PoC plans and success criteria
• Technical presentations and customer demonstrations
• Technical defence presentations

Good to have skills

• Python Programming
• Hand on Kubernetes deployment

Qualifications and Education Requirements

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science or a related discipline.
• Relevant certifications in Cloud, Data Management, Enterprise Architecture, Analytics or AI/ML would be an advantage.