USPB MIS Reporting Re-Engineering & Automation Lead

As a Lead Data Engineer at our company, you will play a crucial role in re-engineering and automating our Management Information Systems (MIS) reporting processes. Your expertise in data engineering, automation, and Big Data technologies, specifically SAS and PySpark, will be essential in collaborating with the MIS reporting team to streamline processes and implement end-to-end automation solutions. Key Responsibilities: - Conduct in-depth analysis of existing manual MIS reporting processes and data flows within the USPB MIS reporting team to identify inefficiencies and opportunities for standardization and automation. - Design and develop robust, scalable, and automated data pipelines and reporting solutions, including ETL/ELT processes, data warehousing solutions, and reporting frameworks. - Implement automation solutions using advanced programming techniques, focusing on SAS and PySpark in a Big Data environment. - Drive the adoption of consistent reporting standards, data governance principles, and best practices across all MIS reporting activities. - Collaborate with the MIS reporting team, business stakeholders, and IT partners to gather requirements and provide technical leadership and mentorship. - Ensure the highest levels of data quality, integrity, and security throughout the automated reporting lifecycle. - Stay updated on emerging data engineering, Big Data, and automation technologies to propose and implement innovative solutions for reporting efficiency and analytical capabilities. - Create comprehensive technical documentation for all developed solutions, data models, and processes. Required Qualifications: - 8-12 years of progressive experience in data engineering, data warehousing, business intelligence, and process automation, preferably in a large financial institution or banking environment. - Extensive hands-on experience with Big Data technologies and ecosystems such as Hadoop, Spark, Hive, and Kafka. - Strong proficiency in SAS programming and demonstrated expertise in PySpark for large-scale data processing and automation in a Big Data context. - Solid understanding of relational databases, SQL, data modeling, and database performance tuning. - Proven track record of designing and implementing end-to-end data automation solutions to improve operational efficiency. - Exceptional analytical, problem-solving, and communication skills. - Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field. The company is committed to providing reasonable accommodations for individuals with disabilities during the application process. Please review the EEO Policy Statement and Know Your Rights poster for more information.,