Senior Data Engineer

NVIDIA•Us, CA

About The Position

NVIDIA's Digital Marketing Operations team is seeking a Senior Data Engineer to build shared data infrastructure supporting customer engagement, campaign intelligence, and marketing analytics at scale. The 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 growth of NVIDIA's marketing technology ecosystem. The engineer will own a key data domain across pipelines, data products, audience services, and consumer teams, shaping its architecture and interfaces while accounting for scale, reliability, performance, privacy, security, cost, and compatibility. The role combines hands-on engineering and technical leadership: building and evolving batch and streaming data pipelines and shared quality frameworks; leading cross-functional initiatives and production incident investigations; embedding privacy and compliance controls into platform design; establishing data models, semantics, ownership boundaries, and serving interfaces; and mentoring engineers while improving engineering quality across teams.

Requirements

8+ years of relevant industry experience, with a Bachelor's degree 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, including end-to-end technical ownership of a multi-pipeline domain or complex cross-team initiative.
Proficiency in Python and one or more modern programming languages such as Java or Golang; strong SQL fundamentals and 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; ability to architect and operate large-scale cloud data workflows.
Experience with data lake and lakehouse architectures, including open table formats such as Apache Iceberg, and 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.
Demonstrated ability to diagnose production failures across distributed systems using logs, metrics, traces, query plans, and profiles, and to implement and validate durable resolutions.
Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, privacy, compatibility, and maintainability.
Experience guiding major migrations or architectural changes across teams without interrupting production service.

Nice To Haves

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.

Responsibilities

Own a key Digital Marketing Operations data domain spanning pipelines, data products, audience services, and consumer teams; shape its architecture and interfaces.
Lead complex, cross-functional initiatives, translating stakeholder requirements into clear data architectures, writing production-quality code, and driving safe production integrations.
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 using 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 to improve pipeline reliability, consistency, and efficiency across teams.
Lead production incident investigations across pipelines, distributed processing, storage, and cloud services; coordinate root-cause analysis, resolution, and preventive improvements.
Embed privacy and compliance by design, including data governance, consent propagation, GDPR and PIPL controls, data minimization, access boundaries, right-to-erasure workflows, and auditable data lineage.
Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams, delivering trusted customer and campaign data assets to marketing consumers.
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.