Note: The job is a remote job and is open to candidates in USA. NVIDIA’s Digital Marketing Operations team is hiring a Senior Data Engineer to build and own shared data infrastructure for customer engagement, campaign intelligence, and marketing analytics. The role leads data pipeline and platform architecture, delivers trusted and privacy-compliant data products, and provides technical leadership across engineering, marketing, and operations teams.
Responsibilities
- 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
- Lead complex, cross-functional initiatives. 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. Coordinate across system owners to establish root cause, drive resolution, and implement preventive improvements
- 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
Skills
- 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
- Demonstrated skill in diagnosing production failures across distributed systems by analyzing logs, metrics, traces, query plans, and profiles, followed by implementing and validating long-lasting 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
- 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
Benefits
- Equity
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