Sr. Data Engineer

GESA CREDIT UNION•Richland, WA

10d•$97,070 - $195,610•Onsite

About The Position

Under the general supervision of the Data Analytics & Governance Manager/Team Leader, the Sr. Data Engineer plays a key role in supporting the enterprise data infrastructure and systems required for data storage, processing, and analysis. While the third-party vendor is responsible for designing the core data warehouse and managing data ingestion from major systems, the Sr. Data Engineer will assist the data warehouse vendor with the establishment of new data sources and provide support for any maintenance and troubleshooting required. This includes focusing on integrating smaller or supplemental data sources into the Azure-based data warehouse and Databricks analytics platform, collaborating with internal teams to identify, model, and automate the ingestion of these data sources, and ensuring they meet organizational standards for data quality, governance, and security. Additionally, the Sr. Data Engineer partners closely with business leaders and executives to help define and advance the vision for managing data as a strategic business asset, aligning data management practices with organizational goals and driving value through effective data stewardship.

Requirements

Advanced knowledge of Databricks for data engineering workloads, including Apache Spark, job orchestration and best practices such as layered data architectures, incremental processing, and optimization techniques
Ability to comfortably utilize modern cloud-based data integration tools within Azure (e.g., Azure Data Factory, Databricks, Synapse Analytics)
Proficiency with a range of programming and scripting languages commonly used in data engineering, including SQL, Python, Scala, Java, Bash or PowerShell, and R.
Advanced understanding of relational and NoSQL databases, data modeling, and data integration
Ability to collaborate across teams and communicate technical concepts to diverse stakeholders
Strong analytical, problem-solving, and debugging skills
Excellent business acumen and interpersonal skills
Commitment to continual process improvement and adherence to data governance standards
Demonstrated leadership capabilities, including coaching and mentoring team members to foster professional growth and promote a collaborative work environment.
Bachelor’s degree in Computer Science, Data Science, Software Engineering, Information Systems, or related quantitative field; Master’s degree preferred
Minimum of six (6) years of experience in data engineering or data management disciplines required

Nice To Haves

Experience designing and operating data pipelines using Databricks in a cloud-based analytics environment preferred
Experience in financial institution environments preferred

Responsibilities

Represent the Data Intelligence department in projects and committees as assigned
Lead the design, development, and optimization of data pipelines for extracting, transforming, and loading data from diverse sources into the third-party data warehouse on Azure
Collaborate with data analysts, BI analysts, data quality and data stewards, and IT professionals to develop and maintain scalable data models and analytical solutions
Integrate data from databases, APIs, and external systems, ensuring consistency, integrity, and quality throughout the process
Implement and monitor data governance frameworks, including data validation, cleansing, aggregation, and enrichment techniques
Establish and enforce data quality checks, validations, and compliance with policies and procedures
Optimize data processing workflows for performance, scalability, and efficiency; resolve bottlenecks and enhance query performance
Design, develop, and maintain scalable data engineering solutions using Databricks, including Spark-based transformations, notebooks, and jobs
Apply Databricks best practices for cluster configuration, job orchestration, performance tuning, and cost-aware design
Implement medallion-style data architectures (Bronze, Silver, Gold) to support governed and analytics-ready datasets
Enable and optimize data pipelines to support AI, machine learning, and advanced analytics use cases, fostering innovation across the organization
Identify opportunities to leverage emerging technologies and innovative data engineering approaches for enhanced business insights and improved decision-making
Support the ongoing strategic planning and monitoring process for data intelligence and data governance
Educate and train stakeholders on data warehouse solutions, data governance, and best practices
Maintain expert-level knowledge of core operating systems, data warehouse platforms, and data intelligence tools