engagement model.
engagement model.
financial operations team
implement data pipelines for efficient data movement and processing (ETL). Ensure data quality through validation techniques. Build and manage scalable data storage and processing systems using distributed frameworks Programming Languages: Python, PySpark, Scala Big Data Processing Frameworks: Apache Hadoop, Spark, Flink Distributed
solutions for remediation. Implement data cleansing and enrichment processes. Be familiar with SAP and be able automate data cleansing processes within SAP. Monitor and improve data governance processes to ensure diagrams, and data lineage. Analyse data management systems and processes to identify areas for improvement
functions, views and triggers for efficient data processing and generate financial data required for revenue
functions, views and triggers for efficient data processing and generate financial data required for revenue
decision-making. Design and implement data models, ETL processes, and data warehouses to facilitate comprehensive documentation for BI solutions, including data models, processes, and user guides. Qualifications: Bachelor's Strong SQL skills and experience with data modeling and ETL processes. Solid understanding of financial principles
data quality and consistency. Implement data governance processes to manage data access, security, and compliance database design principles, data modeling techniques, and ETL processes. Proficiency in SQL, NoSQL databases
handling on large datasets to ensure a robust data monitoring process. Monitor for data quality control issues additional team members to maintain operation data processing. Leveraging the above items listed to monitor Contribute to improving the data engineering best practices and processes. Design agile data models. Implement with complex data relationships and the data mapping process Experience using data migration tools (i Snowpipe, etc.). Understanding of data integration and ETL/ELT processes Familiarity of data architect methods