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# Data Warehouse/BI Engineer

- **Company:** Scor Reinsurance Company
- **Location:** Kansas City, MO, United States
- **Experience:** Experienced
- **Salary:** $107,000.0 - $130,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Unit Testing, Code Review, Continuous Integration, Data Validation, Data Integration, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Scrum Methodology, Software Maintenance, Software Engineering, SQL Databases, SQL Server Integration Services, Systems Integration, Data Logging, Data Processing, Scripting, Performance Testing, Data Ingestion, Pyspark, Information Technology, Data Management, Software Version Control, Databricks
- **Published:** September 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2bf3edd284aa99e5

## About the Role

* BS in Computer Science or related degree or equivalent work experience required
 * 3+ years of working with Databricks, the medallion architecture, Databricks SQL, PySpark, Delta tables, pipelines and parquet files, focused on building and operating ETL/ELT workloads.
 * 3+ years of experience building, enhancing, and supporting large-scale Enterprise ETL processes (data integration, migration, and data quality verification), including Python scripting for automation/reconciliation, with a proven track record of successful deliveries.
 * Strong Python skills for ETL development (scripting, data processing, and automation), including writing reusable modules, implementing logging/error handling, and integrating with scheduling/orchestration.
 * Experience working with data platforms enabling AI solutions. Experience/ Knowledge of the Reinsurance business and/or the financial services industry is a plus
*

## Description

+ Develop, test, and maintain ETL code (PySpark and Python scripts, Databricks notebooks/jobs, and SQL) and implement logging, error handling, and monitoring for reliable daily operations.
 + Identify and evaluate opportunities to integrate artificial intelligence(AI) and predictive analytics into existing data and BI workflows.
 + Partner with business and data stakeholders to identify source systems, define data mappings/business rules, and document transformations and lineage for ETL processes. o Write, maintain, test, and document ETL components (Databricks notebooks/jobs, Python scripts, SQL procedures, and SSIS packages) including unit tests, reconciliation checks, and data quality rules.
 + Collaborate with product owners, solution owners, QA, and other engineers to deliver ETL enhancements end-to-end (requirements, build, test, deploy), and support releases into production.
 + Apply agile delivery practices to ETL work, including backlog refinement, sprint planning, and disciplined testing (data validation, reconciliation, and performance testing).
 + Participate in code reviews and CI/CD for ETL assets (Python/PySpark, SQL, and Databricks jobs) to ensure maintainability, security, and operational stability.
 + Create and maintain Python scripts to automate data ingestion, file handling, and operational tasks (e.g., parameterization, re-runs, alerts), and to generate data quality/reconciliation reports.
 * On software maintenance/Business As Usual support
 + Provide ongoing maintenance, support and enhancements in existing systems and global reporting platforms: troubleshoot and fix software defects, performance issues, and software compatibility problems.
 + Provide production support to systems that host PII data, responding quickly to any production issues that arise.
 + Maintain documentation and version control of software code and related artifacts
 + Implement software security best practices and adhere to industry standards and regulations.
 * On innovation
 + Stay up-to-date with emerging trends, technologies, and software engineering best practices.
 + Provide recommendations for continuous improvement.
 + Work alongside other engineers on the team to elevate technology and consistently apply best practices.
 + Leverage automation and AI tools to improve data quality, data processing and reporting efficiency.