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About the role
Role & responsibilities Enterprise Data Architecture Design end-to-end data ecosystems spanning ingestion, transformation, storage, governance, and visualization Architect scalable cloud-native data platforms for enterprise analytics workloads Define reusable architecture standards, frameworks, and best practices Enable high-performance, secure, and resilient analytics environments Data Engineering & Platform Modernization Build and optimize large-scale ETL/ELT pipelines Design modern data lake, Lakehouse, and warehouse architectures Work across structured, semi-structured, and unstructured datasets Improve data reliability, observability, scalability, and performance Decision Intelligence & Visualization Architect executive-grade BI and visualization ecosystems Architect and oversee implementation of AI/ ML based Agentic Solutions, Gen AI & RAG based systems Build scalable semantic and analytical data models Enable intuitive, business-centric reporting experiences Translate enterprise KPIs and operational requirements into actionable intelligence systems Technical Leadership Guide engineers, BI developers, and analytics teams across architecture and delivery Drive engineering quality through reviews, governance, and technical standards Mentor teams on scalable design thinking and modern analytics engineering practices Collaborate with delivery, business, and leadership stakeholders to align technology with enterprise goals Enterprise Engagement Participate in client workshops, architecture discussions, and transformation roadmaps Contribute to solution proposals, estimations, and technical strategy Act as a trusted advisor for enterprise analytics modernization initiatives Preferred candidate profile Primary Technical Capabilities • Data Engineering Architecture and frameworks • Enterprise Analytics Engineering • Data Platform Modernization • Visualization & BI Architecture • Cloud Data Ecosystems • Performance Optimization & Scalability Platform & Technology Ecosystem • Python / PySpark / SQL • Spark & distributed data processing frameworks • Snowflake / Redshift / Big Query / modern cloud warehouses • AWS / Azure / GCP ecosystems • ETL/ELT platforms such as Informatica, Talend, SSIS, or equivalent • Power BI / Tableau / Qlik / Looker • Data lakes, Lakehouse architectures, and enterprise warehouses • Git, CI/CD pipelines, API integrations Data & Decision Intelligence Exposure • Enterprise data modelling (Star/Snowflake schemas) • Real-time and operational analytics • Decision-support systems • KPI engineering and business intelligence frameworks • Governance, data quality, and analytics reliability Collaboration & Business Capabilities • Enterprise stakeholder communication • Solution consulting and architecture presentations • Cross-functional collaboration • Delivery ownership and execution alignment • Agile and iterative engineering practices
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