Join Popular: Your Career in Data Engineering Awaits

At Popular, we provide a comprehensive range of financial services and solutions that cater to the communities of Puerto Rico, the United States, and the Virgin Islands. Our dedicated employees work tirelessly to help our customers achieve their dreams by offering financial solutions tailored to every stage of their lives. Our commitment to innovation, resilience, and community support, driven by our employees' diverse skills and backgrounds, underlines our core values.

Are You Ready for a Rewarding Career?

Join over 8,000 professionals across Puerto Rico, the United States, and the Virgin Islands who are part of the Popular team. Come and be part of our thriving community!

The Opportunity: Senior Data Engineering Specialist

We are seeking a Data Engineering Specialist to join our Analytical Engineering & Enablement division within the Enterprise Data & Analytics department. This pivotal senior role involves activating AWS services, configuring, validating, and deploying cloud infrastructure specifically designed for analytics and machine learning operations. The specialist will lead a team of data engineers and analysts, fostering a collaborative culture to ensure the efficient processing of data and the optimal performance of machine learning models.

Your Key Responsibilities

• Collaborate with diverse teams to deliver broad data and analytics solutions.
• Guide a dynamic team in creating, validating, and implementing cutting-edge cloud solutions for analytical and machine learning infrastructures.
• Configure various cloud infrastructure components, including networking, security, storage, data migration and processing, and governance analytics.
• Oversee data processing tasks, ensuring datasets are prepped for analysis.
• Form strategic partnerships with data scientists and analysts for cloud environment improvements.
• Design, plan, and build Snowflake environments on AWS Cloud.
• Create Data Lake applications and manage cloud-based data repositories.
• Utilize Terraform or AWS CloudFormation for infrastructure as code.
• Identify and resolve issues related to cloud services, including configuration, integration, and performance.
• Monitor and refine machine learning models for continuous improvement.
• Promote detailed data documentation and integrity.
• Lead the establishment of machine learning development environments ensuring efficient workflows.
• Implement DevOps and DevSecOps methodologies for model deployment.
• Provide high-level support for machine learning systems, ensuring peak performance.
• Conduct knowledge-sharing sessions to enhance team skills.
• Track key performance indicators (KPIs) to drive model refinement.
• Develop best practices, standards, and governance policies.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field; advanced degrees are preferred.
• Minimum 15 years of experience implementing large-scale Data & Analytics platforms in AWS, Azure, or Google Cloud environments.
• At least 5 years of experience in data analytics, machine learning, or a similar leadership role.
• Proven expertise in Hadoop, EMR, EKS, ECS, Docker, Kubernetes, and Amazon Sagemaker.
• Strong proficiency in Python and/or Apache Spark.
• Experience with AWS services including EC2, IAM, AWS S3, API, Lambda, SNS, and SQS.
• Expertise in AWS analytics services like Kinesis, Glue, AWS Batch, EMR, Athena.
• Strong knowledge of Oracle, SQL Server, MySQL, and advanced SQL and PL/SQL skills.
• Proficiency in data mining techniques and machine learning algorithms.
• Advanced skills in Python, R, SQL, and data visualization tools like Tableau or Power BI.
• Hands-on experience with Hadoop, Spark, and other big data platforms.
• Experience with machine learning frameworks like Scikit-learn, TensorFlow, etc.
• Exceptional ability to interpret complex data and communicate insights strategically.
• Commitment to continuous learning and staying updated with industry trends.
• Strong written and verbal communication skills.
• Leadership experience in agile methodologies like Scrum and Kanban.
• Proven ability to manage large and complex datasets.

What We Look For