We are looking for an experienced Database Administrator with deep, hands-on expertise in MongoDB and Elasticsearch to join our data infrastructure team. In this role, you will design, implement, and maintain the database systems that power our platform at scale, ensuring high availability, peak query performance, and airtight data security across large, complex datasets. You will be the internal authority on all things NoSQL and search infrastructure, working closely with backend engineers, data scientists, and DevOps to make data fast, reliable, and accessible.
Responsibilities
- Design, deploy, and manage MongoDB replica sets and sharded clusters in production environments.
- Develop and optimise aggregation pipelines, indexes (single-field, compound, multikey, text, geospatial), and schema designs for performance at scale.
- Monitor query performance using explain plans, the MongoDB Profiler, and Atlas Performance Advisor; identify and resolve bottlenecks proactively.
- Plan and execute data migrations, version upgrades, and schema evolution with zero or minimal downtime.
- Implement and manage Role-Based Access Control (RBAC), field-level encryption, and TLS/SSL configurations in line with security policy.
- Own backup and disaster recovery strategy configuring continuous backups, point-in-time restores, and testing recovery procedures regularly.
- Design and manage Elasticsearch cluster node roles, shard allocation, replication, and index lifecycle management (ILM) policies.
- Build and optimise mappings, analysers, tokenisers, and custom search templates for full-text, fuzzy, semantic, and faceted search use cases.
- Tune cluster performance: JVM heap sizing, thread pool configuration, circuit breaker settings, and cache management.
- Manage data ingestion pipelines using Logstash, Beats, and Kafka connectors; ensure schema consistency across upstream sources.
- Configure Kibana dashboards, alerting rules, and Elastic APM for observability across application and infrastructure layers.
- Implement Elasticsearch Security (TLS, API key management, index-level privileges) and maintain compliance with data retention policies.
- Define and enforce SLOs for query latency, indexing throughput, and cluster availability across both MongoDB and Elasticsearch environments.
- Conduct capacity planning exercises modelling data growth, query load projections, and infrastructure scaling requirements.
- Establish and maintain monitoring, alerting, and on-call runbooks using tools such as Prometheus, Grafana, and Elastic Stack.
- Lead root cause analysis for database incidents and implement permanent fixes with documentation.
- Partner with backend engineers to review data models, query patterns, and API contracts before they reach production.
- Define and enforce database governance standards: naming conventions, indexing policies, data retention schedules, and access control frameworks.
- Mentor junior engineers on database best practices, query optimisation, and operational readiness.
- Maintain comprehensive documentation for all database environments, configurations, runbooks, and architectural decisions.
Requirements
- 3-7 years of hands-on DBA or data engineering experience with MongoDB and Elasticsearch in production environments.
- Deep expertise in MongoDB: replica sets, sharding, aggregation framework, index design, Atlas, and the MongoDB shell/drivers.
- Deep expertise in Elasticsearch: cluster management, mapping design, query DSL, index lifecycle management, and the ELK/Elastic Stack.
- Strong understanding of database internals, storage engines (WiredTiger), write concern, read preference, Lucene segment architecture, and inverted index mechanics.
- Proficiency in at least one scripting language for automation: Python, Bash, or JavaScript (Node.js).
- Experience with cloud-hosted database services: MongoDB Atlas, Elastic Cloud, AWS DocumentDB, or OpenSearch Service.
- Solid understanding of data security principles: encryption at rest and in transit, access control, and audit logging.
- Familiarity with containerisation and orchestration: Docker and Kubernetes for database workloads.
Nice to have
- MongoDB Certified DBA or Developer certification.
- Elastic Certified Engineer or Elastic Certified Analyst certification.
- Experience with vector search capabilities in Elasticsearch (dense_vector, k-NN) or MongoDB Atlas Vector Search for AI/ML use cases.
- Exposure to streaming data platforms: Apache Kafka, Confluent, or AWS Kinesis for real-time ingestion into MongoDB or Elasticsearch.
- Knowledge of complementary data stores: Redis for caching, PostgreSQL or MySQL for relational workloads, or Apache Cassandra.
- Experience with Infrastructure as Code (Terraform, Ansible) for database provisioning and configuration management.
- Exposure to multi-region or multi-cloud database deployments with data sovereignty requirements.
Additional details
- This job was posted by Monisha Raju from Zzazz.
- Technical Skills Snapshot: Primary Databases: MongoDB (replica sets, sharding, Atlas), Elasticsearch (ELK Stack, Elastic Cloud).
- Query & Search: MongoDB Aggregation Framework, Elasticsearch Query DSL, Kibana KQL.
- Monitoring: Prometheus, Grafana, Elastic APM, MongoDB Atlas Monitoring, Datadog.
- Data Pipelines: Logstash, Beats, Kafka, Apache Spark, AWS Glue.
- Cloud Platforms: AWS (EC2 S3 DocumentDB), GCP (Datastore, BigQuery), Azure Cosmos DB.
- DevOps & IaC: Docker, Kubernetes, Terraform, Ansible, GitHub Actions / Jenkins.
- Languages: Python, Bash, JavaScript / Node.js; SQL proficiency.
Zobhira
