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.