Lead enterprise-scale data architecture, governance, and delivery oversight across cloud and AI-enabled ecosystems, driving modern data platforms, MDM excellence, and business-aligned outcomes.
Key Responsibilities
Own end-to-end Data Architecture strategy across enterprise platforms (Cloud, On-prem, Hybrid).
Drive Delivery Governance ensuring quality, timelines, financials, and stakeholder alignment.
Establish and operationalize Data Governance frameworks (data quality, lineage, catalog, compliance).
Lead and enhance Informatica MDM (multi-domain) implementations, upgrades, and optimization.
Architect Cloud Data Platforms (Azure/GCP/AWS) leveraging modern data stack (Data Lakehouse, ETL/ELT, streaming).
Enable AI/GenAI adoption by designing data foundations for ML, analytics, and intelligent automation.
Partner with business and IT leadership to translate data strategy into execution roadmaps .
Define and enforce architecture standards, best practices, and reusable accelerators .
Mentor teams and influence large-scale data transformation programs .
Must-Have Skills & Experience
15+ years in Data Engineering / Data Architecture / Data Platforms
Strong expertise in
Data Governance tools & framework implementation
Proven track record in Delivery Governance & large program leadership
Experience in AI/ML data readiness, GenAI ecosystem exposure
Strong hands-on with
ETL/ELT tools (ADF, Informatica, Databricks)
Data Lakes / Lakehouse architectures
Domain experience in BFSI / Insurance preferred
Executive stakeholder management (CXO-level interactions)
Strong ownership of P&L, delivery excellence, and risk management
Ability to drive innovation, accelerators, and reusable frameworks
High focus on quality, governance, and scalable design
Nice to Have
Experience in MDM modernization / migration programs
Exposure to data privacy regulations (GDPR, etc.)
Certification in Cloud / Data Architecture frameworks
Outcome Expectation (Success Metrics)
Robust, scalable enterprise data architecture in place
Improved data quality, governance maturity, and MDM adoption
Successful delivery of complex data programs with high stakeholder satisfaction
Increased AI/analytics enablement through strong data functions
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Key Responsibilities
Own end-to-end Data Architecture strategy across enterprise platforms (Cloud, On-prem, Hybrid).
Drive Delivery Governance ensuring quality, timelines, financials, and stakeholder alignment.
Establish and operationalize Data Governance frameworks (data quality, lineage, catalog, compliance).
Lead and enhance Informatica MDM (multi-domain) implementations, upgrades, and optimization.
Architect Cloud Data Platforms (Azure/GCP/AWS) leveraging modern data stack (Data Lakehouse, ETL/ELT, streaming).
Enable AI/GenAI adoption by designing data foundations for ML, analytics, and intelligent automation.
Partner with business and IT leadership to translate data strategy into execution roadmaps .
Define and enforce architecture standards, best practices, and reusable accelerators .
Mentor teams and influence large-scale data transformation programs .
Must-Have Skills & Experience
15+ years in Data Engineering / Data Architecture / Data Platforms
Strong expertise in
Data Governance tools & framework implementation
Proven track record in Delivery Governance & large program leadership
Experience in AI/ML data readiness, GenAI ecosystem exposure
Strong hands-on with
ETL/ELT tools (ADF, Informatica, Databricks)
Data Lakes / Lakehouse architectures
Domain experience in BFSI / Insurance preferred
Executive stakeholder management (CXO-level interactions)
Strong ownership of P&L, delivery excellence, and risk management
Ability to drive innovation, accelerators, and reusable frameworks
High focus on quality, governance, and scalable design
Nice to Have
Experience in MDM modernization / migration programs
Exposure to data privacy regulations (GDPR, etc.)
Certification in Cloud / Data Architecture frameworks
Outcome Expectation (Success Metrics)
Robust, scalable enterprise data architecture in place
Improved data quality, governance maturity, and MDM adoption
Successful delivery of complex data programs with high stakeholder satisfaction
Increased AI/analytics enablement through strong data functions
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