Senior Data Management Solution Architect

Senior Data Management Solution Architect

Experience: 10–15 Years

Location: India

Role Type: Solution Architecture + Pre-sales + Advisory

Role Summary

We are seeking a high-impact Data Management Solution Architect to define, architect, and operationalize enterprise data management platforms across Data Governance, Data Quality, Master Data Management, Data Security, and Data Observability.

Role Specifics:

• Owning enterprise data strategy translation into architecture
• Driving modern data architectures (Data Mesh + Data Fabric)
• Enabling AI-ready data ecosystems
• Supporting pre-sales, solutioning, and client advisory
• This role will act as the single-point accountable architect for “trusted data foundation” at scale.
• Work with internal product team to ideate on solution accelerators
• Expert in driving commercial estimations for client proposals

Core Responsibilities

Enterprise Data Management Architecture

Define end-to-end data management architecture across DG, DQ, and MDM layers

Architect multi-domain MDM (Customer, Product, Finance, Reference Data) with golden record strategy, match/merge, survivorship

Design data lifecycle management (ingestion → curation → consumption → retirement)

Publish target-state architecture, standards, and reference models

Data Governance, Security & Compliance

Design enterprise Data Governance operating model (ownership, stewardship, policies)

Implement metadata-driven governance, catalog, lineage, and business glossary

Define and enforce data security (RBAC/ABAC, masking, encryption, PII compliance)

Enable auditability and regulatory compliance (GDPR, DPDP, etc.)

Governance is no longer compliance-only—it must enable trusted AI at scale

Data Quality & Observability Engineering

Define enterprise-wide DQ framework (rules, profiling, monitoring, remediation)

Implement shift-left data quality controls at source and ingestion layers

Establish data observability → freshness, completeness, drift, anomaly detection

Drive DQ automation and self-healing pipelines

Modern Data Architecture (Fabric + Mesh)

Architect Data Fabric layer for unified, metadata-driven access across distributed systems

Design Data Mesh model → domain-driven data ownership, decentralized governance

Enable data-as-product philosophy with domain-level accountability

Integrate data platform, governance, and consumption layers seamlessly

Cloud Data Platform & Engineering

Architect solutions on one leading cloud (Azure / AWS / GCP)

Strong experience in Databricks (Lakehouse) or Snowflake (Data Cloud)

Design scalable data pipelines, lakehouse/warehouse architecture

Ensure performance, scalability, cost optimization, and reliability

AI-driven Data Management

Embed AI/ML in DQ, MDM, and Governance (auto classification, anomaly detection, semantic matching)

Enable AI-ready data foundation (feature stores, lineage, trust layer)

Drive GenAI-led enhancements in data stewardship and metadata automation

Pre-Sales & Client Solutioning

Lead RFP/RFI responses, solution proposals, effort estimation, and architecture definition

Conduct client workshops → assess maturity, define roadmap, propose solution

Create differentiated POVs (AI-augmented DQ, adaptive MDM, metadata automation)

Present to CXO-level stakeholders and drive solution buy-in

Strong ability to map business outcomes ↔ architecture decisions

Leadership & Delivery Governance

Provide technical leadership across programs and engagements

Mentor architects, engineers, and stewards

Drive best practices, reusable accelerators, and COE assets

Ensure alignment with enterprise architecture and governance standards

Mandatory Skills

Deep expertise across:

Data Governance, Data Quality, Master Data Management, Data Security, Data Observability

Strong understanding of:

Data Modeling, Metadata, Lineage, Data Catalogs

Data Integration (ETL/ELT), APIs, Streaming

Experience in:

Databricks Lakehouse or Snowflake Data Cloud

At least one cloud platform (Azure/AWS/GCP)

Good to Have

Presales/consulting experience (RFPs, proposals, estimations)

Experience with tools:

Collibra / Purview / Alation / Unity Catalog

Informatica / Reltio / Profisee / Ataccama / Syndigo

AI/GenAI in data management:

• Semantic matching in MDM
• AI-based DQ automation
• Metadata enrichment
• AI driven automations and solutions

Domain exposure: Telecom / BFSI / Pharma / Manufacturing

Behavioral Expectations

• Strong consulting mindset (problem → solution → value articulation)
• Excellent stakeholder communication (CXO to engineer level)
• Ability to convert ambiguity → structured architecture
• Outcome-driven thinking (business-first, not tool-first)