Data Engineering Expert

6 days ago

Pune Division, India Elongo Global Solutions Full time

🌍 About the Role   We’re looking for a  Data Engineering Expert  to design and build a  financial data pipeline  that connects  SAP BW  and  SAP BFC  to our  Azure Data Platform .  This role is crucial in enabling seamless integration and real-time flow of financial data, which will later power  AI-driven predictive models  for company KPI forecasting.  If you have hands-on experience in  Azure Functions, Kafka, MS Fabric , and deep knowledge of  SAP financial systems , this is your chance to shape a high-impact project that merges  finance, data engineering, and AI innovation . 🎯 Your Mission   You’ll lead the development of an end-to-end  financial data pipeline  — from data ingestion to analytics readiness — ensuring scalability, accuracy, and security across all stages. 🔧 Key Responsibilities   Design & Build Pipelines:  Develop robust and secure data pipelines between  SAP BW ,  SAP BFC , and  Azure Data Platform .  Data Integration:  Streamline data ingestion from SAP systems to Azure using  Azure Functions ,  Kafka , and  MS Fabric .  Optimize Performance:  Ensure efficient, real-time data processing for financial reporting and analytics.  Enable AI Insights:  Structure and prepare data for consumption by  AI models  to predict company KPIs.  Documentation & Support:  Deliver detailed documentation, handover materials, and team training for future maintenance. 🧩 Required Skills & Expertise   Proven experience with  Azure Data Engineering  and  pipeline development .  Strong hands-on knowledge of  SAP BW  and  SAP BFC  (financial consolidation and reporting).  Proficiency in  Azure Functions ,  Apache Kafka , and  MS Fabric .  Solid understanding of  financial data structures  and  ETL processes .  Working knowledge of  Python  for data transformation and automation.  Strong understanding of  data security ,  integrity , and  governance principles . 💡 Nice to Have   Experience integrating  financial ERP data into  cloud-based analytics platforms .  Familiarity with  AI/ML model data preparation or predictive analytics.  Exposure to  real-time data streaming and event-driven architectures. 

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