← coreweave / Staff Product Manager, Data Services
tailored_resume_v2 / art_aHHxWkazEuU
role
model
anthropic/claude-sonnet-4.6
created
2026-05-20T22:09
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What changed for coreweave
| change | why it matters |
|---|---|
| Splunk reordered to lead the Experience section (before Intuit) | Splunk role directly maps to the data services portfolio — Search Catalog (PostgreSQL metadata service), streaming, and query performance are the closest analogs to CoreWeave's databases/metadata/catalog/streaming portfolio |
| Splunk title reframed to 'Search Orchestration & Data Services' | Accurately reflects the scope (Search Catalog, Search Service, SPL) while mirroring JD's 'data services' language |
| Splunk lead bullet rewritten to foreground PostgreSQL metadata catalog and data services portfolio ownership | JD's first hard requirement is 3-4 years focused on data services including managed databases and metadata/catalog — this is the strongest proof point |
| Intuit lead bullet rewritten to foreground 675M engagements / 50K TPS / sub-25ms TP99 metrics | JD emphasizes 'scale, throughput, and performance for GPU-intensive applications' — enterprise-scale proof belongs in first bullet |
| Intuit Asterias bullet reframed as 'metadata catalog and governance' platform | JD explicitly lists 'metadata and catalog' and 'data governance' as portfolio areas — GraphQL API ownership maps to API-first platform thinking |
| Intuit GCP-to-AWS migration bullet reframed to emphasize multi-cloud architecture, data locality, and replication | JD preferred qualification: 'Background in hybrid or multi-cloud architectures: connectivity, data locality, replication' |
| Intuit Drift Detection bullet reframed as 'data governance and catalog integrity' | JD lists data governance as a hard technical requirement; this is the closest accurate mapping |
| Fintellect lead bullet reframed to foreground vector search, LLM serving pipelines, and real-time analytics | JD preferred qualification: 'Experience with AI/ML data products: feature stores, vector search, real-time analytics, or serving pipelines for LLM workloads' |
| StreamIO bullets condensed to 3, reframed around streaming pipeline, tiered pricing, and API-first platform | Maps to JD's streaming/pipelines requirement, usage-based pricing preferred qual, and API-first platform thinking; condensed to save space |
| Kaiser Permanente condensed to 2 bullets foregrounding managed data service at scale and Redis distributed caching | 1.7 TB daily ingest Logging-as-a-Service maps to 'managed databases' requirement; Redis maps to distributed data infrastructure fluency |
| aeval project moved to lead the Projects section | TimescaleDB + Redis + FastAPI stack is the most direct proof of hands-on managed database and streaming pipeline experience — strongest technical credibility for data services role |
| aeval project bullets reframed to foreground TimescaleDB, Redis job queue, API-first architecture, and data governance/quality gates | JD requires relational/distributed database fluency, streaming pipelines, data governance, and API-first platform thinking — aeval demonstrates all four |
| RL Workbench bullet reframed to emphasize GPU-intensive workloads and CoreWeave customer use case | JD's hardest problems are 'scale, throughput, and performance for GPU-intensive applications' — firsthand GPU cluster experience signals customer empathy |
| Summary rewritten to lead with data platform and cloud infrastructure identity, then scale metrics, then AI/ML data products, then credentials | JD's first hard requirement is data platform PM experience; scale proof (675M/50K TPS) and AI data products (vector search, LLM serving) address top hard and preferred requirements |
| IBM bullet reframed to mention 'data platform products' alongside BI | Accurately reflects BI software scope while mirroring JD's data platform language; maintains role without over-inflating |
JD analysis (20 key phrases)
Key phrases: data services portfoliodatabases, data lakes, streaming, metadata and catalogdata sharing and integrationGPU-intensive applicationsscale, throughput, and performanceAPI-first platform thinking0 to 1 and 1 to Nmanaged databaseslakehouse architecturesdata governancereal-time data sharingAI cloudnext-generation AI workloadsoperational excellencecross-functional alignmentcustomer and partner engagementco-designing solutionsmulti-year visionusage-based or tiered pricingcritical infrastructure
Hard requirements:
- 8+ years product management experience in data platforms, databases, analytics, or cloud infrastructure
- 3-4 years focused on data services (managed databases, streaming, data warehouses, data lakes, governance)
- Technical fluency in relational/distributed databases (PostgreSQL, MySQL, SingleStore, Snowflake, BigQuery)
- Streaming and pipelines (Kafka, CDC, ETL/ELT, lakehouse architectures, real-time data sharing)
- Data security and governance (encryption, IAM/RBAC, data masking, network isolation, compliance)
- Track record owning complex API-first platform products from concept through multiple release cycles
- Strong communication skills — clarity from ambiguity, influence without authority
Preferred qualifications:
- Staff or Director-level PM at database vendor, cloud provider, or high-growth infrastructure company
- Hands-on experience with managed data services on AWS, Azure, or GCP (RDS, Aurora, Cosmos DB, BigQuery)
- Background in hybrid or multi-cloud architectures: connectivity, data locality, replication
- Experience with AI/ML data products: feature stores, vector search, real-time analytics, LLM serving pipelines
- Familiarity with SaaS/PaaS packaging and usage-based or tiered pricing models
- Prior engineering or architecture experience on data or distributed systems
Per-role mapping (10 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff Product Manager, Developer Frameworks & Platform Infrastructure | 5/5 | High-scale data and platform infrastructure PM who owned API-first services, multi-cloud migrations, and telemetry-driven prioritization at hyperscale | scale, throughput, and performance, API-first platform thinking, 0 to 1 and 1 to N, multi-cloud architectures, data governance, operational excellence, cross-functional alignment |
| Splunk — Senior Product Manager, Search Orchestration | 5/5 | Data platform PM who owned distributed search, metadata catalog, and query performance at cloud scale — directly analogous to CoreWeave's data services portfolio | databases, data lakes, streaming, metadata and catalog, scale, throughput, and performance, 0 to 1 and 1 to N, API-first platform thinking, managed databases, data services portfolio |
| Kaiser Permanente — SOA Technical Product Manager | 3/5 | Managed data services at enterprise scale with distributed caching and capacity planning | managed databases, scale, throughput, and performance, operational excellence, data services portfolio |
| Streamio AI — Founder & CEO | 3/5 | Founder-built real-time streaming and AI data pipeline platform with tiered pricing and cloud infrastructure | streaming and pipelines, API-first platform thinking, usage-based or tiered pricing, data security and governance, 0 to 1 and 1 to N |
| Fintellect AI — Founder & CEO | 3/5 | Built AI/ML data products including vector search, real-time analytics, and LLM serving pipelines | vector search, real-time analytics, AI/ML data products, LLM serving pipelines, next-generation AI workloads |
| IBM — Software Engineer, Business Intelligence Products | 2/5 | Enterprise data/BI engineering foundation | data platforms |
| Bank of America Merrill Lynch — Tech MBA Summer Associate | 1/5 | Quantitative analytical background | — |
| RL Workbench — Post-Training RL Platform | 4/5 | Built GPU-accelerated AI training platform with real-time streaming metrics and framework benchmarking — directly mirrors CoreWeave's customer workloads | GPU-intensive applications, scale, throughput, and performance, next-generation AI workloads, streaming and pipelines, real-time analytics |
| aeval — AI Model Evaluation Platform | 4/5 | Built local-first data evaluation platform with time-series DB, distributed queue, and API-first architecture | API-first platform thinking, data governance, managed databases, streaming and pipelines, operational excellence |
| BRAIN — Protein Structure Prediction ML Platform | 2/5 | Production ML platform with experiment tracking and distributed orchestration | AI/ML data products |
Tailored summary
Staff Product Manager with 12+ years owning data platforms, cloud infrastructure, and API-first services at scale — from distributed search and PostgreSQL metadata catalogs at Splunk to a 675M-engagement platform running at 50K TPS with sub-25ms latency at Intuit. Deep technical fluency across relational/distributed databases, streaming pipelines, and multi-cloud architectures, with hands-on experience building vector search, real-time analytics, and LLM serving pipelines for next-generation AI workloads. Track record delivering 0-to-1 data services and driving 1-to-N scale in fast-moving, high-growth environments. NeurIPS published; UC Berkeley Engineering.