Minimum 4+ years of hands-on experience in data engineering or data management, preferably in the energy or industrial domain.
Design, develop, and maintain scalable E pipelines to process structured and unstructured data from diverse sources including SAP ECC /, SQL Server, Oracle, other RDBMS, Exc files, Parquet, JSON, XML, and APIs.
Collaborate with business and functional teams to define data integration and transformation strategies aligned with enterprise objectives.
Optimize data pipelines, storage, and query performance for high-volume datasets across cloud or on-prem environments.
•Implement robust data quality, validation, and monitoring frameworks to ensure accuracy and reliability.
Develop and automate data workflows, versioning, and deployment pipelines to streamline data operations.
Support the deployment, monitoring, and governance of data infrastructure and warehou environments.
Work closely with Data Architects to establish best practices for data modeling, warehousing, and lineage tracking.
Enable incremental data processing (CDC) and efficient handling of batch and near real-time data pipelines.
Collaborate with analytics teams to enable insightful visualization and reporting solutions.
Prepare and maintain technical doentation for pipelines, transformations, and data flows.
Collaborate with business stakeholders to understand requirements and ensure alignment with business goals.
Key Deliverables:
•E pipelines for all assigned data sources
•Data ingestion from SAP, databases, files, and APIs
•Bron data layer implementation
•Data quality checks and monitoring setup
•Optimized and scalable data pipelines
•Develop the Power BI dashboard.
•Technical doentation and runbooks
/ Science Graduate in Computer Science, Information Technology, or related field.
Strong foundation in data modeling, performance tuning, and E orchestration.
Experience working with enterprise data sources such as SAP ECC /, S databases, and file-based data (Excel, CSV, Parquet, JSON).
Experience with one or more cloud platforms such as Azure, AWS, or GCP.
Familiarity with tools such as Microsoft Fabric, Databricks, Power BI, or equivalent tools is an advantage.
Experience in data migration or transformation projects is a strong advantage.
Understanding of data governance, data quality, and metada concepts is a plus.
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