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. 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. 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
You'll also build the data infrastructure that AI agents depend on: the embedding
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.

Analyze complex SQL Server stored procedures to understand embedded business logic
and data access patterns

- logic, simplifying data access, improving testability
- Write migration scripts that safely transform database structures while maintaining data

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
Build automated testing for database migrations and refactored procedures
● Document database systems, creating AI-consumable artifacts (structured markdown,
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
Build evaluation and monitoring for RAG components: retrieval accuracy, latency,
Implement semantic search features and retrieval APIs that AI agents and applications
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: Implement data quality checks, validation rules, and observability for data pipelines
- Develop infrastructure-as-code for database provisioning and configuration (Terraform,

Support NoSQL implementations: MongoDB, Cosmos DB document modeling and query
Implement data access patterns that support domain-driven design: repository patterns,
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

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
Contribute to on-call rotation for data platform issues when applicable
● 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
Data pipeline experience: ETL/ELT development, data integration patterns, batch and
Programming proficiency: Python or C# for building tooling, automation, and data
Cloud data services: experience with Azure SQL, Cosmos DB, Snowflake, or AWS data
AI & Vector Database Skills
● Familiarity with vector databases: exposure to Pinecone, Weaviate, pgvector, Azure AI
Experience with or willingness to learn embedding pipeline development

- Active use of AI coding assistants in daily work; database tasks
- Interest in building AI-powered tooling and automation

Robust understanding of database internals: indexing, query optimization, locking,
Experience with event-driven patterns: 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

- 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,

Onboarded to primary database systems; refactoring project; built initial embedding pipeline or vector database implementation; using AI tools in daily work
contributing to AI-powered
database tooling; recognized by team as go-to for complex database problems
AI data
Database engineering is at an inflection point. 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

📌 Senior Database Developer SQL C# (Bengaluru)
🏢 Wex
📍 Bengaluru