About the role
We are looking for a Data Platform/Analytics DBA to manage analytical and data platform technologies supporting reporting, dashboards, search, event streaming, data pipelines, and near-real-time analytics. The role will focus on onClickHouse, Elasticsearch, Kafka, and analytical platform operations, ensuring performance, scalability, monitoring, reliability, data freshness, and platform stability.
Responsibilities:
- Manage and administer ClickHouse environments for analytics, dashboards, reporting, and large-scale query workloads.
- Manage Elasticsearch clusters for search, indexing, application search, logs, and analytical search workloads.
- Manage Kafka platform operations, including topics, partitions, retention, consumer lag, brokers, replication, and cluster health.
- Monitor query performance, ingestion delay, storage growth, failed jobs, data freshness, Kafka lag, index growth, and cluster capacity.
- Tune ClickHouse tables, partitions, ordering keys, materialized views, projections, compression, memory usage, and query performance.
- Tune Elasticsearch indexes, mappings, shards, replicas, lifecycle policies, heap usage, query latency, and indexing performance.
- Manage Kafka topic design, retention policy, replay strategy, consumer group monitoring, and broker-level performance.
- Support ETL/ELT, CDC, Kafka Connect, Airflow, Debezium, and data pipeline operations.
- Troubleshoot production issues related to slow dashboards, query failures, ingestion delays, Kafka lag, Elasticsearch latency, and ClickHouse memory/storage issues.
- Define backup, restore, replication, retention, archival, and DR processes for analytical platforms.
- Work with data engineering, BI, application, infrastructure, and security teams for platform reliability.
- Create operational dashboards, alerts, SOPs, capacity plans, and platform runbooks.
Requirements:
- Strong experience with analytical or data platform administration.
- Hands-on experience with ClickHouse or similar OLAP databases.
- Good experience with Elasticsearch administration, indexing, search performance, and cluster management.
- Good understanding of Kafka operations, topics, partitions, retention, brokers, consumer lag, and replay.
- Strong knowledge of Linux, storage, memory, networking, monitoring, alerting, and production troubleshooting.
- Experience with Airflow, Debezium, Kafka Connect, Grafana, Prometheus, CloudWatch, Superset, Tableau, or Power BI is preferred.