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
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
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
Skills
T-SQL
SQL Server
Python
C#
PostgreSQL
Snowflake
MongoDB
Cosmos DB
Terraform
ARM
Bicep
Azure SQL
AWS
Pinecone
Weaviate
pgvector
Azure AI Search
Kafka
GitHub Copilot
Cursor
Claude Code
Oracle PL/SQL
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.
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.