Career Opportunity: Senior Data Engineer at Popular

At Popular, we are committed to providing a wide range of services and financial solutions to communities in Puerto Rico, the United States, and the Virgin Islands. Our employees are dedicated to helping customers achieve their dreams by offering personalized financial solutions at every stage of their lives. Our long-standing history showcases the resilience and determination of our employees to innovate, find the right solutions, and strongly support the communities we serve. This is why we value diverse skills, experiences, and backgrounds.

Join Our Team

Are you ready for a rewarding career? Over 8,000 people in Puerto Rico, the United States, and the Virgin Islands work with us. Come and be part of our community!

The Opportunity

We are seeking a Senior Data Engineer to join our Analytical Engineering & Enablement team. In this role, you will focus on the in-depth design, development, and implementation of analytical solutions. Your primary responsibilities will include data preprocessing, feature engineering, and ensuring efficient data movement to support informed decision-making and actionable insights. You will tackle advanced statistical analysis and data transformation techniques to address significant business challenges, thereby enhancing our operational efficiency. Your senior position will also involve mentoring and leading initiatives that drive the analytical engineering agenda forward.

Key Responsibilities

• Collaborate with data architecture, governance, security, and various business units, as well as data analysts and other key stakeholders, to define requirements for data and analytical pipelines and understand integration patterns of source systems.
• Set and maintain best practices for data quality, performance, and cost optimization within data and analytical pipelines. Ensure continuous monitoring is in place.
• Guide and manage the Snowflake Center of Excellence (COE), including the creation, evaluation, and selection of ETL/ELT products, as well as optimization and monitoring of these processes.
• Conduct feature engineering both online and offline to enhance the accuracy and performance of analytical models.
• Develop and maintain comprehensive documentation to ensure data processes and models are clear and traceable.
• Implement and uphold data quality rules, standards, and metrics to ensure the accuracy and integrity of analytical results.
• Promote software engineering best practices within the analytics team to ensure reliable and high-quality analytical solutions.
• Apply version control and DataOps principles to ensure the reproducibility and scalability of analytical models and processes.
• Execute thorough data testing to detect and correct errors, inconsistencies, and inaccuracies in data processes and analytical models.
• Utilize appropriate encoding techniques for data preparation to ensure analytical model robustness and efficiency.
• Engage closely with various business units, data engineers, and other stakeholders to understand business challenges, gather requirements, and devise analytical solutions aligned with organizational objectives.
• Incorporate new data sources and methods to enhance the precision and overall effectiveness of analytical solutions.
• Stay abreast of the latest developments and technologies in data science and analytics, integrating cutting-edge techniques where appropriate.
• Perform thorough data analysis and preliminary assessments to uncover trends, inherent patterns, and insights within data.
• Validate the integrity, reliability, and robustness of analytical methods and their outcomes through stringent validation procedures.
• Contribute to AI visualization and user-driven analytics initiatives by developing data visualizations that simplify complex analyses for stakeholders.
• Pursue ongoing education and skill development by learning from experienced data scientists and analysts.
• Ensure strict compliance with data governance, security, and privacy standards.
• Participate in the design, creation, and deployment of analytical models, including predictive analytics, sophisticated clustering algorithms, and machine learning techniques, to analyze complex datasets and extract insights.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, or a related field. A master’s degree is a plus.
• Minimum of 15 years of experience in implementing large-scale Data & Analytics platforms in AWS, Azure, or Google Cloud, on-premises, and hybrid environments.
• Minimum of 5 years of experience in leading and managing functional teams within ED&A such as data integration, data engineering, analytical engineering, BI/data visualization, Data Operations, or a similar role.
• Experience in leading data/analytical engineering teams and delivering data capabilities following various methodologies (waterfall, iterative, scaled agile,