Database Engineer - Multi-Model Data Platform

About the role

A multi-model data platform spanning relational systems, vector and graph databases powers AI-driven applications. The company needs database engineers who take ownership of the operational health of the stack: backup and recovery, patching and upgrades, performance tuning and long-term scalability. This is a hands-on engineering role, not a narrow specialist one, and it is hybrid based in Bangalore.

Key responsibilities

• Define, implement and test backup and recovery strategies across relational, vector and graph databases, including full and incremental backups, point-in-time recovery and disaster recovery runbooks.
• Plan and execute patching and version upgrades with minimal downtime and a documented rollback path.
• Diagnose and resolve performance and scaling issues across queries, indexing, resource use and storage growth.
• Build operational assets such as golden backup templates, provisioning templates, monitoring baselines and runbooks.
• Partner with the platform lead to bring new data technologies to a production-ready standard.
• Provide day-to-day operational support and act as escalation point for data-layer incidents.

Required profile

• 4-8 years of hands-on database engineering or DBA experience, with strong depth in at least one traditional RDBMS (PostgreSQL, MySQL, Oracle or SQL Server).
• Production experience with at least one non-relational system: a vector database (e.g. Milvus, Pinecone, Weaviate, Qdrant) or a graph database (e.g. Neptune, Neo4j, JanusGraph); vector experience preferred but either is acceptable.
• Track record of independently learning and operating a new database technology without formal training or vendor support.
• Solid grasp of distributed data concepts (replication, sharding, consistency trade-offs, indexing strategy).
• Clear, structured communication, with runbooks and trade-off explanations for engineers and stakeholders.

Required skills

• Backup and recovery, performance tuning and upgrade management at production scale.
• Linux environment and scripting (Python or Bash) for automation of backup, monitoring and operational tooling.
• Nice to have: AWS managed database services (RDS, Neptune, DocumentDB), graph query languages (Cypher, Gremlin, SPARQL) or vector search concepts, and prior experience supporting an AI/ML or search-heavy application.

The team is small and high-trust, with a technical lead and a junior engineer; this is an individual contributor role with significant autonomy and direct exposure to architecture decisions.