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Senior, niche enterprise data and SAP skills create moderate applicant density in Bengaluru.
High: role requires deep enterprise systems, SAP, manufacturing and governance domain expertise.
High: explicit 10–15 years plus mandatory Azure/Databricks, SAP and governance expertise.
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Design and guide implementation of enterprise data architecture solutions across cloud, on-prem, and hybrid environments, focusing on scalable, secure, and governed data ecosystems optimized for advanced analytics, GenAI, and ML workloads.
Develop AI-ready enterprise data architecture standards, semantic models, and integration design patterns for multiple enterprise systems including SAP, MES, PLM, CRM, and supply chain domains.
Lead architecture reviews, governance, modernization initiatives and provide technical leadership while collaborating with cross-functional teams to integrate data capabilities with business architecture and digital transformation efforts.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related technical discipline.
10-15 years of experience in data engineering, data architecture, analytics engineering, or enterprise architecture roles, including at least 5 years in enterprise data architecture initiatives.
Deep expertise with modern data platforms such as Azure Data Lake, Delta Lake, Databricks, Synapse, and strong data modeling skills with semantic layering for BI tools.
Strong knowledge of data governance, enterprise system data (SAP ECC/S/4HANA, MES, PLM, etc.), cloud security/compliance, AI/ML data pipelines, and hands-on experience with ETL/ELT pipelines and integration patterns.
Experienced in designing AI-ready and GenAI-supporting enterprise data architectures integrating structured and unstructured data for complex business domains like manufacturing, finance, supply chain, and aftermarket.
Proven ability to lead cross-functional technical teams and influence architecture strategy across cloud, security, governance, and application domains without direct authority.
Skilled at guiding platform modernization (e.g., S/4HANA, SAP BTP), evaluating emerging data technologies, and mentoring data engineering and analytics teams on best practices and reusable data product patterns.