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 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
- 5–8 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.