Senior Data Engineer (Python) - Remote

NVIDIA Corporation; Texas; Teletrabalho; USD 140 000 - 190 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Benefits

Resumo da oferta

NVIDIA is seeking a Senior Data Engineer in its Digital Marketing Operations team to design and operate scalable data pipelines that power customer engagement, campaign analytics, and marketing analytics globally. You will own data domains, ensure privacy by design, and drive architecture across teams.

Responsibilities include building batch and streaming pipelines, implementing data governance, and mentoring junior engineers while collaborating with marketing and operations to translate

Qualificações

• 8+ years of production data engineering experience.
• Experience designing and operating large-scale data pipelines and platforms.
• Strong programming and SQL fundamentals, production-quality code.

Responsabilidades

• Architect, implement, and evolve batch and streaming pipelines at scale.
• Lead data governance, privacy by design, and auditable data lineage.
• Collaborate with engineering, marketing, and operations stakeholders.
• Provide hands-on mentorship and drive architectural decisions in cross-team initiatives.
• Investigate and resolve production incidents across data pipelines and services.

Conhecimentos

Python

SQL

Java or Go

Apache Spark

Airflow

Cloud platforms

Data modeling

Data governance

Streaming systems

Exactly-once processing

Formação académica

Bachelor's degree or equivalent

Master's degree or equivalent

Ferramentas

Airflow

Apache Iceberg

Descrição da oferta de emprego

NVIDIA's Digital Marketing Operations team is seeking a Senior Data Engineer to help build the shared data infrastructure that powers customer engagement, campaign intelligence, and marketing analytics at scale. Our data platform transforms customer behavioral signals, audience definitions, consent records, campaign metadata, and activation outputs into trusted, governed data products for marketing, analytics, personalization, campaign operations, and regional teams worldwide. These products enable audience segmentation, campaign performance measurement, privacy-compliant data activation, identity resolution, and the continued growth of NVIDIA's marketing technology ecosystem. Own a key data domain within Digital Marketing Operations, spanning pipelines, data products, audience services, and consumer teams. Shape its architecture and interfaces while anticipating needs related to scale, reliability, performance, privacy compliance, security, cost, and compatibility.

Collaborate across engineering, marketing, and operations stakeholders to translate requirements into clear data architectures, write high-quality code in the critical path, and drive integrations safely into production.

Architect, implement, and evolve batch and streaming pipelines that ingest, transform, reconcile, and serve customer behavioral, audience, campaign, consent, and activation data at scale. Build reliable, maintainable systems leveraging distributed processing frameworks, workflow orchestration tools, cloud data warehouses, object storage, and pub/sub messaging platforms.

Build and evolve shared data quality frameworks and engineering standards that improve pipeline reliability, consistency, and efficiency across teams.

Lead production incident investigations across pipelines, distributed processing, storage, and cloud services. Embed privacy and compliance by design across the data platform. Implement and enforce data governance, consent propagation, GDPR and PIPL compliance controls, data minimization, access boundaries, right-to-erasure workflows, and auditable data lineage as first-class engineering requirements, not afterthoughts.

Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams. Deliver trusted data assets that make customer and campaign data widely accessible to marketing consumers without sacrificing accuracy, privacy, or maintainability.

Provide technical leadership through architecture and data model reviews, hands‑on mentorship of junior engineers, and evidence‑based resolution of difficult tradeoffs. Raise engineering quality across teams through reusable patterns, clear decisions, and sustained follow‑through.

8+ years of relevant industry experience with a Bachelor's or equivalent experience, and a Master's degree or equivalent in Computer Science, Engineering, or a related field.

• ~ A sustained record of designing, implementing, and operating production data pipelines, data platforms, or distributed data systems. This includes end‑to‑end technical ownership of a multi‑pipeline domain or a complex cross‑team data engineering initiative.
• ~ Proficiency in Python and one or more modern programming languages such as Java or Golang, with strong SQL fundamentals and the ability to write clean, testable, production-quality code.
• ~ Deep hands‑on experience with distributed data processing frameworks such as Apache Spark, and cloud‑managed ETL services. Experience building and operating production pipelines with workflow orchestration tools such as Airflow or equivalent.
• ~ Strong experience with cloud data platform services, including object storage, cloud data warehouses, managed transformation services, and related infrastructure. Demonstrated ability to architect and operate large‑scale data workflows in a cloud environment.
• ~ Experience with data lake and lakehouse architectures, including open table formats such as Apache Iceberg. Ability to design, manage, and optimize large‑scale data storage for reliable analytical consumption.
• ~ Strong SQL and NoSQL skills and data‑modeling proficiency, including practical depth in query execution, incremental processing, schema evolution, consistency, analytical consumption patterns, idempotency, replay, late‑arriving data, partial failure, and cross‑system correctness.
• ~ Experience building systems that move customer, behavioral, consent, campaign, or audience data across multiple platforms, with strong attention to data quality, governance, and operational reliability.
• ~ Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, privacy, compatibility, and maintainability. Experience with MarTech stacks and B2C customer data at scale, including CDP integrations, reverse ETL, identity resolution, customer 360 or profile services, audience activation pipelines, campaign metadata, tracking code management, and multi‑touch attribution.
• Experience designing and operating high‑throughput, fault‑tolerant streaming systems at scale, including exactly‑once delivery semantics, consumer lag management, schema registry patterns, and replay strategies for large data volumes.

If you're creative and autonomous, we want to hear from you!

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 23, 2026. NVIDIA uses AI tools in its recruiting processes.

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.