Sr. Data Engineer

UMB Bank•Kansas City, MO

About The Position

The Enterprise Data Warehouse & Analytics team supports the data warehouse environment and analytics tools, giving business leaders access to business intelligence data to develop organizational growth strategies. The Sr. Data Engineer will implement Snowflake cloud strategies, modernize legacy systems, build real-time reporting solutions, and develop future data-processing capabilities using technologies including AWS, Confluent Kafka, Aurora PostgreSQL, Snowflake, Matillion, and Informatica. The role will also support multiple initiatives assigned by IT leadership. The position designs and maintains cloud data pipelines, architectures, and data sets for ETL and streaming; develops PySpark ETL jobs and AWS Lambda workflows; optimizes Aurora PostgreSQL schemas and processes; and builds real-time reporting and replication solutions. It also supports data governance, lifecycle management, Kafka event processing, evaluation of data trends, and organizational standards for data collection, storage, arrangement, and use. The engineer mentors team members in collaboration with the senior architect while helping modernize technologies and assess complex data trends.

Requirements

Bachelor’s degree in Information Technology and at least 5 years of experience in a technical role supporting and designing application technologies, or at least 8 years of relevant work experience in such a technical role.
At least 5 years of cloud data development or engineering experience, specifically using Azure or AWS, with hands-on experience deploying and managing Snowflake data platforms.
At least 5 years of experience with data pipeline development and business intelligence implementations.
Subject matter expertise in modern data platform architectures, including Apache Kafka for streaming, PostgreSQL database management, and Precisely for data integration and replication.
A business-first mindset, comfort with change, and a passion for learning and using modern technologies, databases, code sets, and integration best practices.
Legal authority to work in the United States; work visa sponsorship is not available.

Nice To Haves

Banking or financial services industry experience.

Responsibilities

Design, build, and maintain cloud data pipelines, architecture, and data sets for ETL and real-time streaming using Kafka, Precisely, and Snowflake.
Build and optimize PySpark ETL jobs for S3-to-Aurora data loading using metadata-driven mappings and field validation, and collect performance metrics.
Develop and maintain AWS Lambda functions and EventBridge schedules and rules to orchestrate pipeline workflows, manage operational status, and send alerts.
Develop and optimize Aurora PostgreSQL schemas, stored procedures, real-time and batch-load processes, indexes, and queries; troubleshoot database and data-processing issues.
Build and manage high-performance relational database structures, with a focus on PostgreSQL replication, optimization, and real-time reporting pipelines.
Implement data governance and lifecycle management strategies, including policies and automated approaches for data flows.
Implement organizational models to oversee data collection, storage, and utilization.
Design and support Kafka topics, partitions, retention policies, and access controls to meet data-volume and availability needs.
Build and maintain producers and consumers that reliably publish and process events, preserving key-based ordering where required.
Mentor team members in collaboration with the senior architect and apply cloud expertise to modernize technologies and evaluate complex data trends.
Evaluate, assess, and translate data, identifying anomalies, trends, and correlations among similar data sets.
Create models and standards governing what data is collected, how it is stored and arranged, and how it is used in data systems and across the organization.