Langoor
Website:
langoor.com
Job details:
About
Langoor, A 'born in digital era' digital marketing agency is challenging/changing the very way marketers make sense of the digital disruption and navigate their way to Impact Business x Marketing x Brand Performance.
Going beyond the elementary impact of digital in terms of search, social and storytelling, marketers need to look at their customer journeys in the digital domains and focus their action based on insights of digital behaviour and contextise their marketing mix strategies and action.
Driving Indian Brands into the Digital First Marketing Era with our expertise in Data x Technology x Strategic Thinking x Creative Edge delivering agile Marketing Transformations & Brand Performance.
About the Role
We operate a multi-model data platform spanning traditional relational systems alongside vector and graph databases that power AI-driven applications. As we scale, we need database engineers who can take ownership of the operational health of this stack: backup and recovery, patching and upgrades, performance tuning, and long-term scalability planning. This is a hands-on engineering role, not a narrow specialist role. You will work across PostgreSQL and emerging systems such as Milvus (vector) and Amazon Neptune (graph), with additional technologies such as Cognee likely joining the stack over time.
We are not looking for someone who already has years of production experience in every technology on this list — for some of these systems, that experience barely exists in the market. We are looking for someone with strong database fundamentals and the demonstrated ability to become the operational expert on an unfamiliar data system quickly, with minimal external support.
What You Will Own
- Define, implement, and test backup and recovery strategies across relational, vector, and graph database systems, including full and incremental backup, point-in-time recovery, and disaster recovery runbooks.
- Plan and execute patching and version upgrades for production database systems with minimal downtime and a documented rollback path.
- Diagnose and resolve performance and scaling issues across query performance, indexing, resource utilization, and storage growth.
- Build repeatable operational assets: golden backup templates, provisioning templates, monitoring baselines, and runbooks that reduce reliance on any single person's tribal knowledge.
- Partner with the platform lead to assess new data technologies as they are introduced and bring them to a production-ready operational standard.
- Provide day-to-day operational support and act as an escalation point for data-layer incidents.
What You Bring
- 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) covering backup/recovery, performance tuning, and upgrade management at production scale.
- Working 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 database experience is preferred over graph, but either is acceptable.
- A track record of independently learning and operationalizing a new database technology without formal training or vendor support – be ready to describe a specific instance of this in interviews.
- Solid grasp of core distributed data concepts (replication, sharding, consistency trade-offs, indexing strategy) that transfer across relational and non-relational systems.
- Comfort working in Linux environments, with scripting ability (Python or Bash) for automation of backup, monitoring, and operational tooling.
- Clear, structured communication – you will be documenting runbooks and explaining trade-offs to both engineers and stakeholders.
Nice to Have
- Exposure to AWS-managed database services (RDS, Neptune, DocumentDB, or similar).
- Familiarity with a graph query language (Cypher, Gremlin, or SPARQL) or vector search concepts (embeddings, ANN indexing) beyond the required hands-on system.
- Prior experience supporting an AI/ML or search-heavy application from the data layer.
What Success Looks Like in the First 6 Months
- A documented, tested backup and recovery strategy is in place for Milvus, with recovery time objectives validated through an actual restore drill.
- A patching and upgrade process exists for at least one non-relational system in the stack, with a rollback plan that has been tested at least once.
- You are trusted to independently triage and resolve a performance or scaling issue on an unfamiliar system with only documentation and your own investigation.
Team Context
You will join a small, high-trust data platform team consisting of a technical lead and a junior engineer, with this role and one additional hire expanding the team's operational capacity. This is an individual contributor role with significant autonomy and direct exposure to architecture decisions.
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