Database Systems Team Lead - Applied AI Engineering Group

It starts with you - a technical leader whos passionate about database systems and growing high-performing teams. You care about query performance, uptime, data durability, and operational excellence. Youll 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 mission - this role is for you.
: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.