Database Systems Team Lead - Applied AI Engineering Group

At Dream, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; it’s a Dream job. Dream is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Let’s build something extraordinary together.

Dream's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Central to our Dream's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.

At Dream, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.

The Dream Job:

It starts with you - a technical leader who’s passionate about database systems and growing high-performing teams. You care about query performance, uptime, data durability, and operational excellence. You’ll lead the Datastores team in operating, tuning, and scaling 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 lead a team that keeps the database engines running for mission-critical AI systems, join Dream’s mission - this role is for you.

The Dream-Maker Responsibilities:

• Lead and grow the Datastores team - hiring, mentoring, and developing engineers while fostering a culture of operational excellence.
• Own database reliability and availability - ensuring systems meet demanding SLAs for government and national-scale customers.
• Drive performance tuning and optimization - query analysis, index strategies, configuration tuning, and resource optimization across all database engines.
• Establish operational practices - backup/recovery procedures, disaster recovery, replication strategies, and failover automation.
• Plan and execute capacity management - monitoring growth, forecasting needs, and scaling databases ahead of demand.
• Lead incident response for database issues - troubleshooting, resolution, and post-incident improvements.
• Enable new capabilities - evaluating, deploying, and operating new database technologies including vector databases for AI workloads.
• Partner with Data Platform, Data Engineering, Engineering, and Security teams to align database operations with platform needs.
• Define and uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate on schema evolution, partitioning, and safe replay/backfills.
• Integrate with catalog and lineage systems - surfacing ownership, change history, and impact analysis for critical datasets and collections.
• Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to prioritize performance, durability, and evolution of data stores across workloads.
  
  

• 8+ years in database administration, database engineering, or storage infrastructure, with 2+ years leading teams or technical functions. 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
• Cloud & managed services - AWS RDS, Aurora, ElastiCache, OpenSearch Service; managed vs self-hosted trade-offs