Analyze, refactor, and modernize complex SQL Server stored procedures and embedded business logic.

Write migration scripts, preserve data integrity, and implement CDC, outbox, and event-publishing patterns.

Optimize queries, execution plans, indexes, locking, and database performance.

Build embedding pipelines, vector database implementations, synchronization processes, semantic search, and RAG retrieval infrastructure.

Develop ETL/ELT pipelines and data integrations across SQL Server, PostgreSQL, Snowflake, cloud data services, and NoSQL systems.

Implement database provisioning and configuration using infrastructure-as-code such as Terraform and ARM/Bicep.

Create data quality checks, validation, observability, automated migration tests, and deployment workflows.

Use AI coding assistants and develop AI-powered tooling for stored procedure analysis, schema documentation, and migration assistance.

Partner with application and AI/ML engineers, participate in reviews and on-call support, document solutions, and mentor junior engineers.

8–12 years of experience in database engineering or data platform roles, including strong SQL Server experience.

- Deep T-SQL proficiency covering complex queries, stored procedures, functions, performance tuning, and execution-plan analysis.
- Hands-on experience modernizing legacy database code through refactoring and migration.
- Experience developing ETL/ELT workflows, data integrations, batch processing, and streaming workflows.




- Programming proficiency in Python or C# for tooling, automation, and data processing.
- Experience with Azure SQL, Cosmos DB, Snowflake, or AWS data services.
- Familiarity with vector databases such as Pinecone, Weaviate, pgvector, or Azure AI Search.
- Understanding of embeddings, RAG concepts, similarity search, and retrieval patterns, with experience or willingness to learn embedding pipeline development.
- Solid knowledge of database internals, indexing, query optimization, locking, and transaction isolation.
- Experience with event-driven patterns, Kafka, infrastructure-as-code, version control, database migrations, schema versioning, and deployment automation.
- Familiarity with NoSQL document databases and key-value stores.
- Preferred qualifications include regulated-industry experience, Oracle PL/SQL experience, hands-on RAG or semantic-search development, database tooling or open-source data contributions, and data observability experience.

The role includes a 90-day, 6-month, and 12-month development roadmap covering onboarding, stored procedure modernization, embedding or vector implementations, production RAG infrastructure, AI tooling, architectural contributions, and mentoring.

Contributes to AI-native data infrastructure and legacy modernization work with exposure to database engineering, vector technologies, and AI-assisted development.

Participation in an on-call rotation for data platform issues may be required.

📌 Software Development Engineer, AWS (Bengaluru)
🏢 Wex
📍 Bengaluru