Database Engineer

About the team / Role Hands-on database engineering role focused on implementing, optimizing, and modernizing data systems across the technology stack. Works closely with Database Architects to execute modernization initiatives—refactoring stored procedures, building data pipelines, implementing vector databases, and developing AI-powered tooling. This is an execution-heavy role where you'll write code daily: T-SQL, Python, infrastructure-as-code, and whatever else is needed to get data systems working well. We are seeking a Senior Database Engineer to join our data engineering team. You'll work on two fronts: modernizing legacy SQL Server systems (decomposing complex stored procedures, optimizing performance, migrating business logic to services) and building AI-native data infrastructure (embedding pipelines, vector database implementations, RAG components). This is an AI-first engineering role. You'll use AI coding assistants daily to accelerate your work—analyzing stored procedures, generating migration code, debugging query performance issues. You'll also build the data infrastructure that AI agents depend on: the embedding pipelines, vector indexes, and retrieval systems that make RAG work. If you enjoy the craft of database engineering—writing elegant queries, optimizing execution plans, building reliable pipelines—and want to apply those skills to both legacy modernization and cutting-edge AI infrastructure, this role is for you. How you'll make an impact Stored Procedure Refactoring & Legacy Modernization Analyze complex SQL Server stored procedures to understand embedded business logic and data access patterns Refactor stored procedures following architect-defined patterns: extracting business logic, simplifying data access, improving testability Write migration scripts that safely transform database structures while maintaining data integrity Implement event-driven patterns: change data capture (CDC), outbox tables, and event publishing from database changes Optimize query performance: analyze execution plans, design indexes, refactor inefficient queries Build automated testing for database migrations and refactored procedures Document database systems, creating AI-consumable artifacts (structured markdown, annotated schemas) alongside traditional documentation AI Data Infrastructure Implementation Build and maintain embedding pipelines: text extraction, preprocessing, chunking, embedding generation, and vector storage Implement vector database solutions: configure indexes, optimize similarity search, implement hybrid retrieval patterns Develop data synchronization processes that keep vector stores current with source systems Build evaluation and monitoring for RAG components: retrieval accuracy, latency, freshness metrics Implement semantic search features and retrieval APIs that AI agents and applications consume Work with AI/ML teams to optimize embedding strategies and retrieval quality Data Platform…