Stockman Bank→
DATA ENGINEER at Stockman Bank in Billings, MT
Mid LevelOn-siteFull-timeBillings, MT
Job Description
DATA ENGINEER
Officer Full-Time · Location: Billings, MT, US
This position does not have remote capabilities.
Position Summary
The Data Engineer supports the Bank's enterprise data environment by building and maintaining data pipelines, preparing data for reporting and analytics, and improving data quality and reliability. This role works closely with Technology, Operations, and business teams to move data from banking systems into secure, reliable, and usable reporting structures.
The position is hands-on and execution-focused, supporting data warehousing, reporting, analytics, governance, and emerging artificial intelligence initiatives. The Data Engineer is expected to possess strong analytical and problem-solving skills, attention to detail, and the ability to manage multiple priorities while delivering accurate and dependable data solutions.
Data Engineer Responsibilities
Data Pipeline Development
- Build and maintain data pipelines for reporting and analytics.
- Support data ingestion from core banking and related business systems.
- Write, test, and maintain SQL queries, stored procedures, and scripts.
- Document data flows, source data, and transformation logic.
Data Platform Support
- Support the Bank's data warehouse, lakehouse, and reporting data structures.
- Assist with Microsoft Fabric, Power BI, and related data platform initiatives.
- Monitor scheduled jobs and resolve data processing issues.
- Improve performance, reliability, and repeatability of production data processes.
Banking Systems Integration
- Work with data from core banking, digital banking, credit card, mortgage, financial, and third-party systems.
- Help define secure and maintainable data feeds.
- Coordinate with system owners, vendors, and technology teams to investigate and resolve data issues.
Data Governance Enablement
- Apply data quality controls and resolve data exceptions.
- Maintain metadata, data definitions, and source documentation.
- Support data governance efforts and regulatory requirements.
- Ensure adherence to security, privacy, and data management standards.
Analytics Platform Support
- Prepare trusted datasets for Power BI dashboards, reports, and management reporting.
- Partner with business teams to understand reporting requirements and data needs.
- Automate recurring reporting processes and reduce manual data preparation.
- Support future analytics, machine learning, and AI initiatives through improved data readiness.
Team Collaboration
- Work closely with Technology, Operations, and business teams.
- Communicate technical issues in clear and practical business terms.
- Follow established standards for change management, security, and documentation.
- Continue developing expertise in data architecture, governance, and cloud-based data platforms.
Qualifications
Technical Qualifications
- Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related technical discipline, or an equivalent combination of education and work experience.
- Two to four years of experience in data engineering, business intelligence, database development, reporting, or data warehouse support.
- Strong SQL skills.
- Experience building or supporting ETL/ELT processes.
- Working knowledge of relational databases and data modeling concepts.
- Experience with Power BI or similar reporting platforms.
- Python, PowerShell, or similar scripting experience preferred.
- Microsoft Fabric or Azure Data Factory experience preferred.
- Ability to troubleshoot data issues and document findings clearly.
Data and Banking Knowledge
- Understanding of data warehouse design and reporting structures.
- Familiarity with dimensional modeling, data quality, data lineage, and metadata concepts.
- Core banking systems experience preferred.
- Regulatory reporting experience preferred.
- Financial institution data environment experience preferred.
- Banking operations process knowledge preferred.
Success Measures
- Reliable data pipelines and scheduled data processing jobs.
- Improved data quality and reduced reporting exceptions.
- Clear documentation of data sources, transformations, and business logic.
- Reduced manual effort in recurring management reporting.
- Continued growth in banking data knowledge and platform capabilities.