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 Engineering
Design and implement data pipelines for ETL/ELT workflows across SQL Server, PostgreSQL, Snowflake, and cloud data services
Build and maintain data integration patterns: API-based ingestion, event streaming, batch processing
Implement data quality checks, validation rules, and observability for data pipelines
Develop infrastructure-as-code for database provisioning and configuration (Terraform, ARM/Bicep)
Support NoSQL implementations: MongoDB, Cosmos DB document modeling and query optimization
Implement data access patterns that support domain-driven design: repository patterns, query services, read models
AI-Assisted Engineering
Use AI coding assistants (GitHub Copilot, Cursor, Claude Code) daily for stored procedure analysis, code generation, and debugging
Develop prompts, scripts, and workflows that leverage AI for database engineering tasks
Contribute to AI-powered tooling: stored procedure analyzers, schema documentation generators, migration assistants
Create AI-consumable artifacts: structured schemas, annotated procedures, context files for AI agents
Help evaluate and adopt new AI tooling for database engineering
Collaboration & Quality
Partner with application engineers to design data access patterns that meet performance and scalability requirements
Participate in code reviews for database-related changes, ensuring quality and consistency
Contribute to on-call rotation for data platform issues when applicable
Document solutions and contribute to team knowledge bases
Mentor junior engineers on database engineering practices
Experience you will bring
8 -12 years in database engineering or data platform roles, with strong SQL Server experience
Deep T-SQL proficiency: complex queries, stored procedures, functions, performance tuning, and execution plan analysis
Hands-on refactoring experience: you've modernized legacy database code, not just maintained it
Data pipeline experience: ETL/ELT development, data integration patterns, batch and streaming workflows
Programming proficiency: Python or C# for building tooling, automation, and data processing scripts
Cloud data services: experience with Azure SQL, Cosmos DB, Snowflake, or AWS data services
AI & Vector Database Skills
Familiarity with vector databases: exposure to Pinecone, Weaviate, pgvector, Azure AI Search, or similar
Understanding of embeddings and RAG concepts: how text becomes vectors, how similarity search works, basic retrieval patterns
Experience with or willingness to learn embedding pipeline development
Active use of AI coding assistants in daily work; understanding of effective prompting for database tasks
Interest in building AI-powered tooling and automation
Technical Depth
Strong understanding of database internals: indexing, query optimization, locking, transaction isolation
Experience with event-driven patterns: CDC, Kafka, event sourcing concepts
Infrastructure-as-code: Terraform, ARM templates, or similar for database provisioning
Version control and CI/CD for database changes: migrations, schema versioning, deployment automation
Familiarity with NoSQL: document databases, key-value stores, when to use what
Preferred Experience
Background in healthcare, benefits, payments, or similarly regulated industries
Experience with Oracle PL/SQL in addition to SQL Server
Hands-on RAG implementation or semantic search development
Contributions to database tooling or open-source data projects
Experience with data observability tools: query monitoring, performance dashboards, alerting
In 90 days: Onboarded to primary database systems; completed first stored procedure refactoring project; built initial embedding pipeline or vector database implementation; actively using AI tools in daily work
In 6 months: Independently leading stored procedure modernization for assigned systems; RAG/vector infrastructure you've built is in production use; contributing to AI-powered database tooling; recognized by team as go-to for complex database problems
In 12 months: Measurable impact on stored procedure modernization velocity; AI data infrastructure supporting production agent workflows; mentoring junior engineers; contributing to architectural patterns and standards
Why This Role Matters
Database engineering is at an inflection point. Legacy systems need modernization—but we can now use AI to analyze, understand, and migrate complex database code faster than ever. AI applications need purpose-built data infrastructure—and database engineers who understand both traditional data systems and vector/embedding technologies are rare.
You'll work on both sides: using AI to accelerate legacy modernization while building the data layer that AI applications depend on. The skills you develop here—combining deep database craft with AI-native infrastructure—will be increasingly valuable as every organization grapples with these same challenges.
