• Implement a functional SSAS environment. • Building and re-designing a set of reports that need to transition to an analysis services environment. • Work with our customer’s technical team to identify initial requirements. • Apply tabular, relational, and multidimensional data modeling methodologies. • Transform raw data into meaningful business decisions and pre-aggregate data for optimal results. • Document logical and physical data models. • Document and update reporting environment data model. • Develop new reports based on customer requirements

• Analysis, Design, implementation, and maintenance pipelines that produce business-critical data reliably and efficiently using cloud technologies. • Develop new ETLs (Extract, Transform, Load), using the current Apache Airflow . • Propose new initiatives to improve performance, scalability, reliability, and overall robustness. • Collect, process, and clean data from different sources using Python & SQL. • Work side by side with the main Architects and Developers to create and ensure best practices and guidelines are being used properly by all projects. • Assess and communicate effectively the effort for required developments. • Discover new data sources to improve new and existing data pipelines. • Be in charge of building and maintaining data pipelines and data models for new and existing projects. • Maintain detailed documentation of your work and changes to support data quality and governance. • Provide feedback and expert points of view as needed to help all data initiatives in the company. • Improve the quality of existing and new data processes (ETL), incorporating statistical process control, and creating alerts when anomalies are received from data sources at every step of the pipeline. • Create benchmark control of execution times for every pipeline, to control and identify potential availability issues.

• Design and implement scalable data architecture and end-to-end ETL/ELT pipelines • Analyze diverse data sources and create high-quality, structured data layers • Lead large-scale data migrations to cloud-based platforms (Azure, AWS, GCP) • Optimize complex SQL queries and database performance • Collaborate with analysts, data scientists, and business stakeholders • Ensure data governance, quality, and regulatory compliance • Mentor junior engineers and promote engineering best practices

• Deploy and manage AI evaluation processes across our infrastructure. • Process and perform comprehensive data analysis to identify trends, inconsistencies, and opportunities for model enhancement. • Build and maintain scalable data pipelines. • Collaborate with cross-functional teams to embed security and compliance considerations into the development lifecycle. • Automate repetitive workflows and implement solutions to improve operational efficiency.