Build and operate reusable batch and streaming ingestion capabilities for service provider and internal data. Establish supported patterns for APIs, change data capture, streams, database connectors, and files, including orchestration, retries, backfills, and recovery.
Establish shared identifiers, data contracts, consistent modeling, and automated quality and certification capabilities. Partner with source owners to define dataset meaning, keys, supported joins, historical coverage, compatibility, and ownership. Make quality failures actionable and dataset limitations visible to consumers.
Guide cataloging, lineage, and governed access capabilities that help people and AI agents discover datasets, understand their definitions and relationships, and follow approved access procedures.
Make onboarding and operations repeatable through shared libraries, reference implementations, deployment automation, and self service workflows that teams can adopt in their repositories. Measure improvements in onboarding effort, dataset reuse, and manual toil.
Own production readiness and reliability standards, including SLOs, testing, observability, runbooks, rollback, and recovery. Lead critical investigations across pipelines, applications, SQL engines, Spark, storage, networks, and cloud services. Partner with security and infrastructure teams on service identities, least privilege, secrets management, environment isolation, auditability, and upgrades and migrations that preserve data access and production service.
Mentor senior engineers and technical leads, lead design reviews, and resolve technical disagreements through evidence and clear tradeoffs. Partner with leadership on priorities, progress, operational risks, and investment decisions.
What we need to see
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
More than 16 years of progressively responsible experience developing and operating production software, data platforms, databases, or distributed systems.
A sustained record of defining technical strategy and architecture for shared platforms spanning multiple teams, with accountability for delivery, adoption, and production outcomes. Demonstrated judgment balancing reliability, performance, security, cost, and maintainability through testing and evaluation of alternatives.
Extensive hands on experience with distributed data processing, analytical databases, production ETL, streaming systems, or backend platforms handling substantial data volumes. Strong understanding of dependencies and failure modes across compute, storage, networking, and cloud services.
Strong software engineering fundamentals and production proficiency in Python or another backend or systems language, with the ability and willingness to work primarily in Python and SQL. Experience designing reusable frameworks and personally implementing and debugging critical code paths.
Deep SQL and data modeling expertise, including query execution, incremental processing, schema evolution, and consistency. Experience connecting independently managed systems through shared identifiers, documented relationships, and reliable data contracts, with clear ownership and compatibility standards. Ability to reason about idempotency, replay, late arriving data, partial failure, and correctness across system boundaries.
Experience establishing production practices adopted by multiple teams, including CI/CD, data validation, observability, incident response, secure deployment, and recovery. Demonstrated ability to lead complex production investigations and major migrations, using logs, metrics, traces, query plans, profiles, and controlled experiments to identify root causes and prevent recurrence.
Demonstrated success influencing technical direction across organizational boundaries, aligning engineering and business leaders around shared goals, and developing engineers for broader technical responsibilities.
Ways to stand out from the crowd
Extensive experience developing and managing Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, or Unity Catalog platforms, including common features applied across engineering teams.
Experience delivering self-service data publishing, cataloging, lineage, data contracts, or certification systems with demonstrated adoption.
Experience combining operational data from various cloud providers and internal systems, including entity resolution, inventory reconciliation, lifecycle history, and coverage measurement.
Experience designing and operating Kafka or comparable streaming systems, including partitioning, consumer behavior, offset management, backpressure, replay, and schema compatibility.
Experience enabling AI agents to discover and use governed data, or building production agentic systems with tool integration, evaluation, permissions, observability, and failure recovery.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 13, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
