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 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.