Description: * Prior experience in Data Engineering (commensurate with the seniority level of the position), working on building data pipelines and integration processes in production environments. * Proficiency with ETL and data integration tools, particularly hands-on experience with Pentaho Data Integration, to develop data extraction, transformation, and loading (ETL) jobs. * Strong programming skills, especially in Python, for data manipulation and workflow automation. * Solid SQL expertise and experience with relational databases (designing complex queries, query optimization, data modeling). * Experience with cloud-based data environments (Cloud Computing), e.g., hands-on work with data platforms and services on AWS, Azure, or GCP, including cloud-based data storage and processing. * Knowledge of data architecture and Data Warehousing, including dimensional and conceptual modeling, understanding of ETL/ELT processes, and best practices for building reliable data pipelines. * Problem-solving and root-cause analysis skills applied to data pipelines, with ability to debug job failures, handle exceptions in ETL processes, and ensure integrity of delivered data. * Develop and maintain scalable ETL/ELT data pipelines, performing data extraction, transformation, and loading from multiple sources using Pentaho Data Integration as the primary platform. * Integrate new data sources into existing systems, ensuring data quality, consistency, and availability for use by analytics and business teams (e.g., Power BI dashboards). * Optimize and monitor data process performance, performing necessary tuning and adjustments to ensure efficient, reliable, low-latency pipelines. * Collaborate with the Automation Architect and BI/Analytics teams, understanding data requirements from automation and reporting projects, to deliver solutions aligned with those needs. * Implement data governance, security, and compliance practices, ensuring pipelines comply with data protection policies and that data quality is continuously verified (e.g., monitoring failures, missing data, etc.). * Document developed data processes and workflows, maintaining clear records of transformations, pipeline configurations, and architecture to facilitate maintenance and future enhancements. 2512190202551809507