Sr. Worldwide GTM Specialist - DynamoDB, Data & AI GTM

About the role

WS DynamoDB GTM is seeking a Senior Worldwide GTM Specialist to accelerate adoption of Amazon DynamoDB as the backbone of modern, high-scale application architectures. This role drives strategic GTM initiatives positioning DynamoDB as the default choice for operational workloads requiring single-digit millisecond latency, infinite scalability, and zero operational overhead - from internet-scale applications to agentic AI data layers. Technical Focus: DynamoDB architecture: single-digit millisecond reads/writes at any scale, on-demand vs. provisioned capacity modes, global tables for multi-region active-active deployments Data modeling patterns: single-table design, adjacency lists, composite sort keys, GSI overloading, and sparse indexes for complex access patterns Migration pathways: relational-to-DynamoDB modernization (Oracle/SQL Server/MySQL → DynamoDB), dual-write patterns, CDC-based migration using DMS, and DynamoDB Import for bulk data loading Integration with AI/ML workloads: DynamoDB as session state, vector metadata store, and tool-use data layer for agentic AI architectures (MCP, NL2SQL gateway patterns) Zero-ETL and streaming: DynamoDB Streams, Kinesis Data Streams integration, export to S3 for analytics, and zero-ETL to Redshift/OpenSearch Operational excellence: DAX (in-memory caching), TTL-based data lifecycle management, point-in-time recovery, and cost optimization strategies GTM Characteristics: Strategic scope: Worldwide motion - define and scale repeatable DynamoDB engagement patterns across geos, segments (ENT, STRAT, SMB), and verticals Migration-first pipeline: Owns the GTM motion for relational-to-DynamoDB modernization - the highest-ARR pipeline driver for the service Cross-functional execution: Bridges DynamoDB product/engineering, field specialists, SAs, and account teams to align on unified positioning against competitors (MongoDB Atlas, GCP Firestore/Bigtable, Azure Cosmos DB) Field enablement: Builds scalable programs - sales plays, migration assessment tools, battle cards, proof points, and field motions that convert discovery into pipeline Customer engagement: Comfortable in deep technical discovery with architects and engineering leaders; can whiteboard single-table designs, discuss partition key strategies, and articulate why DynamoDB eliminates operational overhead vs. self-managed NoSQL Pipeline & adoption metrics: Directly accountable for DynamoDB new workload adoption, migration pipeline growth, and cross-service attach (DynamoDB → Streams → analytics) Competitive positioning: Technical fluency to counter MongoDB ("developer familiarity"), Cosmos DB ("multi-model"), and Firestore ("serverless simplicity") with trust-based differentiation rooted in DynamoDB's operational track record at scale Data and AI GTM - Senior Worldwide GTM Specialist Job Summary: The AWS Data and AI GTM team is seeking a Senior Worldwide GTM Specialist to drive cross-service go-to-market strategies that connect AWS's data platform (databases, analytics, storage) with AI/ML workloads - particularly agentic AI, generative AI, and foundation model integration patterns. This role sells across service boundaries, positioning the full AWS data platform as the differentiated foundation for AI-ready enterprises. Technical Focus: Data foundations for AI: how databases (Aurora, DynamoDB, Neptune, OpenSearch) and analytics (Redshift, Athena, Glue, EMR) enable retrieval-augmented generation (RAG), vector search, knowledge graphs, and agentic workflows Agentic AI architecture patterns: MCP (Model Context Protocol), tool-use frameworks, NL2SQL, semantic layers, and AI-database integration Data quality, governance, and lineage as prerequisites for trustworthy AI outputs Reverse-attach motion: AI workloads (Bedrock, SageMaker) driving new data platform consumption Cross-service value prop: zero-ETL pipelines, unified data access, and the platform advantage vs. single-service competitors (Snowflake, Databricks, GCP Spanner/AlloyDB, Azure Fabric) GTM Characteristics: Cross-service storytelling: Breaks down service team silos - articulates the unified AWS data platform advantage that no competitor can match at scale (1M+ customers trust AWS databases) Play-based execution: Operates within structured sales plays (e.g., "Data Foundations for AI" - $653M+ Created ARR) and emerging plays (DAAP / Data and Agentic AI Accelerator Pathways) Customer maturity model: Engages customers at different stages - from data modernization (crawl) to AI-ready architectures (run) - with stage-appropriate messaging Competitive positioning: Technical fluency to counter GCP (AlloyDB/Spanner), Azure (Cosmos DB/Fabric), and Snowflake/Databricks with honest, trust-based differentiation Metrics-driven: Tracks AI attach rates (DB→AI, AI→Data reverse attach), pipeline velocity, and cross-service consumption as success indicators Field scalability: Builds enablement assets, working sessions, and prospecting day motions that help field team

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