Data and Platform Engineer

NVIDIA’s DGX Cloud organization is seeking a Senior Data Engineer to become part of its data team! We develop the reliable data foundation that supports fleet health, capacity, utilization, cost, reliability, and operational decision-making throughout DGX Cloud. Our platform supports engineering, operations, finance, and product teams managing and expanding large GPU fleets across cloud service providers and NVIDIA Cloud Partners. We are looking for a practical engineer and technical lead to take charge of a key part of the Navigator data platform. We develop the systems that transform distributed infrastructure telemetry and operational data into dependable, managed data products that support fleet health, capacity, utilization, cost, and operational decisions. You will be responsible for the architecture, technical plan, and production results of a major platform area like ingestion and orchestration, data quality and reconciliation, or data serving and consumption. You will clarify requirements with customers, define technical objectives, guide design and development among engineers and partner teams, and stay actively engaged in coding, debugging, and production tasks. Successful candidates have already led complex technical work across team boundaries and delivered improvements that other groups adopted. Our primary implementation environment is Python, SQL, Databricks, and Spark.

What you’ll be doing: You will own a major platform component and its roadmap. For example, define its architecture, interfaces, technical goals, and evolution. Anticipate capacity, compatibility, and operational needs over a multi-year horizon, and translate them into achievable breakthroughs that balance immediate delivery with long-term maintainability. Lead technical delivery across teams. Work with customers and interested parties to clarify vague requirements. Break down design and implementation work for contributing engineers. Establish release turning points and manage dependencies and delivery risks. Guide the work process, revise plans when requirements shift, and keep management and partner teams informed and aligned. Build data pipelines and products. Plan and carry out batch and streaming ingestion, transformation, reconciliation, and serving processes for fleet, capacity, utilization, cost, scheduling, and operational telemetry. Establish data models and agreements that remain stable as sources, consumers, and scale progress. Develop shared platform capabilities. Direct the creation and adoption of libraries, workflow and DAG or comparable experience abstractions, deployment tools, and standard implementation approaches. Partner with related teams to solve shared challenges and evaluate progress in onboarding time, engineering effort, reliability, and cost. Lead complex production investigations. Serve as the technical point of accountability for issues spanning pipelines, applications, SQL engines, Spark, storage, networks, and cloud services. Coordinate investigations across owners, drive resolution of release blockers and critical issues from partners, and implement preventive measures. Define quality, security, and operational expectations. Establish and implement testing, data-quality, reconciliation, lineage, SLO, and release-readiness standards for your platform area. Partner with security and infrastructure teams on trust boundaries, service identities, least privilege, secrets, environment isolation, and auditability, and drive adoption across contributing teams. Make trusted data usable. Deliver well-modeled tables, APIs, automation, dashboards, and focused internal applications. Align with consumers on semantics, access patterns, freshness, compatibility, and ownership so that shared capabilities support dependable operational decisions. Provide technical leadership through others. Guide design reviews, mentor engineers taking on larger ownership, and resolve technical disagreements using evidence and clear tradeoffs. P