Senior 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.