5 years working as a database engineer or in a related role.
Bachelor's in Computer Science strongly preferred.
Strong SQL expertise. You can write a query to do almost anything.
Experience with Python strongly preferred.
Hands-on experience using AI coding assistants and Developer Productivity tools (e.g., Cortex, Code Assist, Claude Code) for coding, debugging, and SQL generation.
Strong AI literacy: Ability to effectively prompt, validate, debug, and audit AI-generated code and SQL queries for accuracy, efficiency, and security.
ETL experience is a must, DBT preferred.
Experience with data warehouse platforms, Snowflake preferred.
Understanding of various raw data sources (flat file, ODBC, XML, JSON) and data flows from raw to warehouse formats.
Understanding of common data warehouse best practices.
Experience working in production systems.
Must comply with HIPAA rules and regulations (including safe usage guidelines for AI tools when handling sensitive data or proprietary schemas).
Experience with both MySQL and Microsoft SQL Server.
A strong desire to learn new technologies and adapt to AI-assisted development workflows.
Willingness to accept input from anyone on the team.
Ability to manage multiple priorities in a fast-paced, dynamic environment.
Strong attention to detail.
Growth mentality, motivated to develop into a key member of the team.
Strong problem-solving skills.
Excellent written and verbal communication skills.
Ability to work and thrive in a strong team environment.
Adaptable and flexible.
Responsibilities
Design and develop high-volume, low-latency applications for mission-critical systems, delivering high-availability and performance.
Leverage AI coding assistants (e.g., Cortex, Code Assist, Claude Code) to accelerate code generation, optimize SQL/Python scripts, write automated tests, and streamline refactoring.
Contribute in all phases of the development lifecycle.
Write well-designed, testable, efficient codecombining human code review with AI-generated suggestions to ensure security and maintainability.
Identify opportunities to make our database faster by running diagnostics, optimizing queries, building indexes, and performance tuning.
Test and track bugs at the raw data level, utilizing AI debugging and root-cause analysis tools to identify data quality issues faster.
Work in an Agile development process and tools (JIRA/git/etc.). You can address bugs with QA, plan schemas with the rest of the engineering team, and respond quickly to other business needs.
Troubleshoot potential issues proactively.
Database tuning.
Work with stakeholders to gather requirements and break down work into manageable tasks.
Actively experiment with new AI tools and workflows to improve personal and team engineering velocity.
