Developer Relations Manager – Data Processing and Databases

NVIDIA•Us, CA

23h

About The Position

NVIDIA is building GPU acceleration for the analytical data processing ecosystem, focusing on query engines, data platforms, and processing frameworks. This role involves embedding an acceleration layer into engines not owned by NVIDIA, addressing the challenge of integrating with diverse optimizers, schedulers, memory models, and exchange mechanisms. The position requires deep expertise in analytical data systems, including query planning and processing, columnar and vectorized processing, storage formats, and the internals of SQL engines. The successful candidate will collaborate with maintainers, architects, and engineering leaders of these systems to integrate acceleration, produce benchmark evidence, and translate learnings into NVIDIA's roadmap. This is a developer relations role focused on broad ecosystem influence rather than deep engineering in a single engine.

Requirements

Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, or a related field.
6+ years of overall professional experience in software engineering, developer relations, technical partnerships, solutions architecture, or product management.
Hands-on experience with analytical data systems.
Equivalent evidence of domain authority (e.g., published systems research, maintainership of a widely used data system, core contributions to a query engine or data processing library) is weighed in place of years.
Experience working with or supporting open source data projects and their contributor communities, commercial data platform and database ISVs, or cloud service provider data services.
Working proficiency in the internals of analytical data systems: query execution and optimization, vectorized and columnar processing, joins and aggregation, and storage formats such as Parquet and Arrow.
Comfortable reading and contributing to a large C++, Rust, or Python codebase.
Comfort collaborating with cross-functional teams to discuss architecture, share feedback, and deliver technical presentations or demos.
Ability to manage and implement technical projects, solve integration challenges, and effectively communicate complex ideas to both technical and non-technical audiences.
Strong communication skills and a passion for helping developers innovate with NVIDIA tools and technology.

Nice To Haves

Committer, maintainer, or sustained contributor to a widely used open source data system.
Shipped an acceleration layer or engine integration into a commercial data platform, end to end.
Published or presented systems work at venues such as VLDB, SIGMOD, CIDR, or major open source community conferences.
Experience serving as technical counterpart to partner engineering leadership, including architecture review, design review, and joint roadmap planning.
Hands-on familiarity with advanced computing and GPU acceleration platforms, including CUDA, RAPIDS, cuDF, nvCOMP, and related CUDA-X libraries.

Responsibilities

Build and deepen technical expertise in analytical data processing, including query execution and optimization, columnar and vectorized processing, and distributed execution.
Serve as a technical advocate and trusted resource for developers building and operating analytical data systems, driving adoption of NVIDIA technologies.
Demonstrate and integrate NVIDIA's data processing stack into real query engines and OLAP databases across various deployment models.
Support developers, projects, and partners through onboarding and integration by providing reference implementations, guides, and hands-on engineering help.
Track the analytical data processing ecosystem, including new engines, execution models, storage formats, and competing acceleration approaches.
Share learnings with NVIDIA engineering, product, marketing, and field organizations to shape adoption strategy.
Collaborate with engine architects and NVIDIA engineering to resolve integration problems, establish best-practice patterns, and feed technical requirements back to product teams.
Define the integration surface between NVIDIA's GPU data processing libraries and third-party query engines.
Specify the APIs NVIDIA must expose for integration and propagate successful patterns across integrations.
Own the benchmark evidence for GPU-accelerated analytics, designing and running measurements against CPU baselines.
Publish results that demonstrate the acceleration case, identify areas for improvement, and define necessary changes.
Own the acceleration roadmap jointly with projects and partners, determining priorities and technical bets.
Earn standing through upstream contributions, public design reviews in open source, and joint architecture/roadmap planning with partner engineering leadership.