It starts with you – an engineer driven to operate database systems at the highest level of reliability and performance. You care about query latency, uptime, data durability, and getting paged as little as possible. Youll operate, tune, and scale the database engines that serve the platform – from PostgreSQL to Elasticsearch, Redis to vector databases, across cloud and on-prem environments.
If you want to run the databases that power mission-critical AI at national scale, join our companys mission – this role is for you.
The Responsibilities
Operate and maintain database systems – PostgreSQL, Elasticsearch, Redis, MongoDB, vector databases, and others across cloud and on-prem.
Own database reliability – high availability configurations, replication, failover automation, and SLA adherence.
Drive performance tuning – query optimization, index design, configuration tuning, and resource profiling to meet latency and throughput targets.
Execute operational procedures – backup/recovery, disaster recovery testing, upgrades, migrations, and capacity scaling.
Lead incident response for database issues – troubleshooting production problems, root cause analysis, and implementing preventive measures.
Build monitoring and alerting – dashboards, metrics collection, slow query analysis, and proactive capacity alerts.
Enable new capabilities – deploying and tuning vector databases for AI workloads, evaluating new database technologies.
Collaborate with Data Platform, Data Engineering, Engineering, and Security teams on database operations and best practices.
Uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate safe schema evolution, partitioning, and replay/backfill practices.
Expose catalog and lineage signals – ownership, change history, and impact analysis – to improve trust and safe consumption for downstream users.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to balance performance, durability, and evolution across workloads.
If you want to run the databases that power mission-critical AI at national scale, join our companys mission – this role is for you.
The Responsibilities
Operate and maintain database systems – PostgreSQL, Elasticsearch, Redis, MongoDB, vector databases, and others across cloud and on-prem.
Own database reliability – high availability configurations, replication, failover automation, and SLA adherence.
Drive performance tuning – query optimization, index design, configuration tuning, and resource profiling to meet latency and throughput targets.
Execute operational procedures – backup/recovery, disaster recovery testing, upgrades, migrations, and capacity scaling.
Lead incident response for database issues – troubleshooting production problems, root cause analysis, and implementing preventive measures.
Build monitoring and alerting – dashboards, metrics collection, slow query analysis, and proactive capacity alerts.
Enable new capabilities – deploying and tuning vector databases for AI workloads, evaluating new database technologies.
Collaborate with Data Platform, Data Engineering, Engineering, and Security teams on database operations and best practices.
Uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate safe schema evolution, partitioning, and replay/backfill practices.
Expose catalog and lineage signals – ownership, change history, and impact analysis – to improve trust and safe consumption for downstream users.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to balance performance, durability, and evolution across workloads.
Requirements:
6+ years in database administration, database engineering, or storage infrastructure, with hands-on experience operating databases at scale.
Relational databases – PostgreSQL, MySQL; replication (streaming, logical), partitioning, connection pooling (PgBouncer), vacuum tuning, query plan analysis
Document & search engines – Elasticsearch, OpenSearch, MongoDB; cluster operations, shard management, index lifecycle, query optimization
Caching & key-value stores – Redis, DynamoDB, ScyllaDB; cluster modes, persistence options, eviction policies, memory optimization
Vector databases – Milvus, Qdrant, pgvector; index types (HNSW, IVF), similarity search tuning, embedding storage
Operations & reliability – Backup strategies, point-in-time recovery, disaster recovery, high availability configurations, failover testing
Performance tuning – Query optimization, index design, configuration tuning, resource profiling, slow query analysis
Monitoring & observability – Database metrics, alerting, capacity dashboards, performance trending.
6+ years in database administration, database engineering, or storage infrastructure, with hands-on experience operating databases at scale.
Relational databases – PostgreSQL, MySQL; replication (streaming, logical), partitioning, connection pooling (PgBouncer), vacuum tuning, query plan analysis
Document & search engines – Elasticsearch, OpenSearch, MongoDB; cluster operations, shard management, index lifecycle, query optimization
Caching & key-value stores – Redis, DynamoDB, ScyllaDB; cluster modes, persistence options, eviction policies, memory optimization
Vector databases – Milvus, Qdrant, pgvector; index types (HNSW, IVF), similarity search tuning, embedding storage
Operations & reliability – Backup strategies, point-in-time recovery, disaster recovery, high availability configurations, failover testing
Performance tuning – Query optimization, index design, configuration tuning, resource profiling, slow query analysis
Monitoring & observability – Database metrics, alerting, capacity dashboards, performance trending.
This position is open to all candidates.
