> Markdown version of [/jobs/ext/4214640-database-engineer](https://www.wearedevelopers.com/jobs/ext/4214640-database-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Database Engineer - **Company:** Mitchell International, Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $71,677 - $114,982 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Architectural Patterns, Automation of Tests, Code Review, Databases, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Warehousing, Python (Programming Language), PostgreSQL, Microsoft SQL Server, Online Analytical Processing, Online Transaction Processing, OpenFlow, Operational Databases, Oracle (Applications), Performance Tuning, Power BI, Salesforce.Com, SQL Databases, Tableau (Software), Cloud Platform System, Data Ingestion, GitHub Copilot, Snowflake, Git, Containerization, Information Technology, Star Schema, Data Management, Software Version Control, Data Pipelines, Docker - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/database-engineer--e3b97622-8e11-41b5-8c29-c56468928c85 ## About the Role Database Programming, Fortune 500 Customers, Clinical Medicine, Leading Edge Technology, Team Player, Data Management, Data Warehousing, Snowflake Schema, Data Quality, Salesforce.com, * 3-5 years of hands-on experience building data pipelines in production cloud environments * Strong proficiency in SQL, including performance tuning and window functions * Working knowledge of Python for data pipeline development and automation * Experience with modern data stack technologies including Snowflake, dbt, data ingestion tools (e.g. Snowflake Openflow, Fivetran), orchestration tools (e.g. Dagster, Airflow), AI-assisted development (e.g. Snowflake CoCo, GitHub CoPilot) * Familiarity with OLTP databases (e.g. PostgreSQL, Oracle, SQL Server), with understanding of the differences between OLTP and OLAP systems * Experience working with AWS cloud services for data workloads (S3 or similar) * Familiarity with CI/CD concepts and version control workflows (e.g. Git, pull requests, automated testing) * Strong communication skills with the ability to explain technical work to non-technical stakeholders * Proactive and self-motivated, comfortable working in a fast-paced environment with evolving priorities Preferred Skills: * Bachelor's degree in Computer Science, Data Engineering, or a related technical field * Experience with Medallion Architecture * Experience with using containerization technologies such as Docker and Kubernetes to deploy and manage data applications * Experience with analytics platforms like Hex and/or BI tools such as Power BI or Tableau * Understanding of the insurance industry, particularly workers' compensation or P&C insurance ## Description As a Data Engineer in the Casualty Analytics Group, you will build and maintain the data infrastructure that powers Enlyte's analytics and decision-making capabilities. You'll join a growing engineering team and be responsible for developing robust data pipelines that integrate data from multiple source systems into our Snowflake data warehouse, ensuring data quality, reliability, and accessibility for downstream analytics. You'll have the opportunity to take on increasing ownership as the team and platform mature, * Build and maintain production data pipelines using Snowflake and dbt to integrate and transform data from various sources (e.g. SQL Server, Oracle, PostgreSQL, Salesforce) * Implement data models and transformations following established architectural patterns and team standards * Collaborate with business stakeholders and senior engineers to understand data requirements and deliver high-value integrations * Write and maintain documentation for pipelines, data models, and engineering processes * Contribute to data governance and quality practices, including testing, monitoring, and alerting * Participate in code reviews, both giving and receiving feedback to maintain pipeline quality * Build proof-of-concepts and MVPs that demonstrate value for larger data initiatives