Inteliment Technologies - Technical Architect - Data Engineering

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.