Database Engineer

Overview

Work as a Senior Database Engineer focused on modernizing legacy SQL Server systems and building AI-native data infrastructure. The role involves daily coding with T-SQL, Python, infrastructure-as-code and other tools to implement, optimize and modernize data systems.

What you'll do

• Analyze complex SQL Server stored procedures to understand embedded business logic and data access patterns.
• Refactor stored procedures following architect-defined patterns by extracting business logic, simplifying data access and improving testability.
• Write migration scripts that safely transform database structures while maintaining data integrity.
• Implement event-driven patterns including change data capture, outbox tables and event publishing from database changes.
• Optimize query performance by analyzing execution plans, designing indexes and refactoring inefficient queries.
• Build automated testing for database migrations and refactored procedures.
• Document database systems by creating AI-consumable artifacts such as structured markdown and annotated schemas alongside traditional documentation.
• Build and maintain embedding pipelines covering text extraction, preprocessing, chunking, embedding generation and vector storage.
• Implement vector database solutions, configure indexes, optimize similarity search and implement hybrid retrieval patterns.
• Develop data synchronization processes that keep vector stores current with source systems.
• Build evaluation and monitoring for RAG components, including retrieval accuracy, latency and freshness metrics.
• Implement semantic search features and retrieval APIs consumed by AI agents and applications.
• Work with AI/ML teams to optimize embedding strategies and retrieval quality.
• Design and implement ETL/ELT data pipelines across SQL Server, PostgreSQL, Snowflake and cloud data services.
• Build and maintain API-based ingestion, event streaming and batch processing data integration patterns.
• Implement data quality checks, validation rules and observability for data pipelines.
• Develop infrastructure-as-code for database provisioning and configuration using Terraform and ARM/Bicep.
• Support MongoDB and Cosmos DB implementations, including document modeling and query optimization.
• Implement data access patterns supporting domain-driven design, including repository patterns, query services and read models.
• Use GitHub Copilot, Cursor and 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 including stored procedure analyzers, schema documentation generators and migration assistants.
• Create AI-consumable artifacts including structured schemas, annotated procedures and context files for AI agents.
• Help evaluate and adopt new AI tooling for database engineering.
• Partner with application engineers to design data access patterns meeting performance and scalability requirements.
• Participate in code reviews for database-related changes, ensuring quality and consistency.
• Contribute to the on-call rotation for data platform issues when applicable.
• Document solutions and contribute to team knowledge bases.
• Mentor junior engineers on database engineering practices.

What you'll need

• 5–8 years in database engineering or data platform roles, with strong SQL Server experience.
• Deep T-SQL proficiency, including complex queries, stored procedures, functions, performance tuning and execution plan analysis.
• Hands-on experience refactoring legacy database code, not just maintaining it.
• Data pipeline experience covering ETL/ELT development, data integration patterns, batch workflows and streaming workflows.
• Programming proficiency in Python or C# for building tooling, automation and data processing scripts.
• Experience with Azure SQL, Cosmos DB, Snowflake or AWS data services.
• 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.
• Strong understanding of database internals, including indexing, query optimization, locking and transaction isolation.
• Experience with event-driven patterns including CDC, Kafka and event sourcing concepts.
• Infrastructure-as-code experience with Terraform, ARM templates or similar for database provisioning.
• Version control and CI/CD experience for database changes, including migrations, schema versioning and deployment automation.
• Familiarity with NoSQL, including document databases, key-value stores and when to use what.
• Familiarity with vector databases, including exposure to Pinecone, Weaviate, pgvector, Azure AI Search or similar.
• Understanding of embeddings and RAG concepts, including how text becomes vectors, how similarity search works and basic retrieval patterns.

Nice to have

• 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 including query monitoring, performance dashboards and alerting.

Details

• Location: Bangalore, India.
• The role includes contribution to an on-call rotation for data platform issues when applicable.