We are building a multi-model data platform combining PostgreSQL, Milvus (vector) and Amazon Neptune (graph), with technologies such as Cognee likely to be added over time.

This is a hands-on database engineering role focused on backup & recovery, patching/upgrades, performance tuning, scalability and production operations across relational and emerging data systems.

We are not looking for expertise in every technology listed. Strong database fundamentals and the ability to quickly learn and operationalize unfamiliar database technologies independently are key.

What You Will Own

- Design, implement and test backup, recovery and disaster recovery strategies, including full/incremental backup and point-in-time recovery.
- Plan and execute patching and version upgrades with minimal downtime and tested rollback plans.
- Troubleshoot performance, indexing, resource utilization and storage/scaling issues.
- Build reusable backup templates, provisioning templates, monitoring baselines and operational runbooks.
- Evaluate and operationalize new database technologies as they enter the platform.
- Provide day-to-day operational support and act as an escalation point for data-layer incidents.

What You Bring
- 3–8 years of hands-on database engineering/DBA experience with strong depth in PostgreSQL, MySQL, Oracle or SQL Server, covering backup/recovery, performance tuning and production upgrades.




- Production experience with at least one vector database (Milvus, Pinecone, Weaviate, Qdrant) or graph database (Neptune, Neo4j, JanusGraph). Vector database experience preferred.
- Proven ability to independently learn and operationalize a new database technology.
- Strong understanding of replication, sharding, consistency trade-offs and indexing strategies.
- Strong Linux skills and scripting experience with Python or Bash for automation.
- Transparent communication and documentation skills, with the ability to explain technical trade-offs.

Nice to Have
- AWS database services: RDS, Neptune, DocumentDB or similar.
- Graph query languages: Cypher, Gremlin, SPARQL.
- Vector search concepts: embeddings, ANN indexing.
- Experience supporting AI/ML or search-heavy applications from the data layer.

Success in the First 6 Months
- Tested Milvus backup and recovery strategy, including an actual restore drill.
- Documented and tested patching/upgrade and rollback process for at least one non-relational system.
- Ability to independently troubleshoot performance or scaling issues on unfamiliar systems.

Team Context

You will join a small, high-trust data platform team with significant autonomy and direct exposure to architecture decisions. This is an individual contributor role working closely with the technical lead and junior engineer.

Please write to [email protected] to get connected!

📌 Database Engineer – Polyglot Data Platforms (Bengaluru)
🏢 CareerXperts Consulting
📍 Bengaluru