Software Dev Manager, DDB Index and Search

Software Development Manager, DynamoDB Vector Search

Amazon DynamoDB is looking for a Software Development Manager to lead a new initiative building native vector search, full-text search, and hybrid retrieval . You will lead engineers working at the intersection of distributed databases, information retrieval, and AI infrastructure, enabling customers to search their operational data without building and managing a separate search stack.

The technical decisions are substantial. How should vector indexes be organized across a changing, partitioned dataset? How do selective filters affect approximate nearest-neighbor retrieval? How should semantic and keyword results be combined? How do you deliver high recall and predictable tail latency while data is continuously inserted, updated, and deleted?

Your team will tackle these questions across the search stack-from index structures and storage integration to distributed query execution and relevance evaluation. The work includes evaluating graph-based and clustering-based indexing approaches, compression, caching, text analysis, and ranking. You will guide decisions about what to build, how to measure it, and how to evolve the architecture as customer workloads grow.

As the SDM, you will own the team's roadmap, engineering execution, and operational readiness. You will work with senior and principal engineers to resolve architectural questions, establish measurable launch criteria, and turn exploration into production capabilities. You will hire and develop engineers, build technical leadership within the team, and create an environment where engineers can make decisions and carry them through to customer outcomes.

This is an opportunity to shape a new database capability from its foundational architecture through launch, helping developers build semantic discovery, recommendations, and retrieval for generative AI applications directly on DynamoDB.

Key job responsibilities

• Lead and grow an engineering team building vector, full-text, and hybrid search; develop engineers with depth in search algorithms, database internals, and distributed execution.
• Own the technical roadmap, sequencing algorithm evaluation, integration, performance qualification, and customer delivery into achievable milestones.
• Guide architecture for vector and text indexes, including choices among retrieval algorithms, memory and disk layouts, compression, and update strategies.
• Lead delivery of query planning and execution across partitions, including filter placement, candidate selection, ranking, and combining results.
• Establish evaluation practices that reveal how recall, relevance, latency, and cost change with dataset size, filter selectivity, write rate, and workload skew.
• Partner with storage and database teams to support search under continuous data changes, infrastructure failures, and growing workloads.
• Make deliberate scope and staffing decisions, resolve dependencies, and ensure testing and rollout plans develop alongside implementation.
• Own production readiness and team health, including observability, deployment safety, incident learning, and sustainable on-call practices.