Staff Data engineer

About the role

Position Summary...

What you'll do... Staff Data Engineer designs and builds high-scale platforms processing billions of advertising and customer signals across streaming, near-real-time, micro-batch, and batch workloads.

Owns complex data domains from problem definition and design through production delivery and optimization.

Engineers reliable systems with predictable latency, throughput, replayability, idempotency, fault recovery, and cost efficiency.

Develops reusable, tenant-aware frameworks with standardized schemas, contracts, validation, observability, privacy, and deployment patterns.

Modernizes pipelines supporting reporting, audience activation, measurement, optimization, and ML.

Enables governed agentic workflows for discovery, development, monitoring, troubleshooting, documentation, and operational automation while improving engineering standards and delivery velocity across teams.

About the team:

Global AdTech and MarTech builds data and decisioning platforms powering advertising across Walmart U.S., Sam’s Club, and international markets.

Connects identity, intent, audiences, campaigns, inventory, bidding, relevance, creative, transactions, attribution, and measurement at massive scale.

Enables Sponsored Products, Display, Connected TV, offsite, social, and in-store advertising, along with reporting, targeting, optimization, forecasting, recommendations, and analytics.

Operates platforms processing billions of events and tens of billions of bids daily.

Designs for latency, correctness, resilience, privacy, scalability, tenant isolation, and cost efficiency.

Advances AI and agentic advertising through trusted, governed, reusable data foundations for advertisers, customers, and internal business teams.

What you'll do:

- Own major capabilities across advertising events, campaigns, identity, audiences, measurement, reporting, bidding, recommendations, forecasting, CRM, and marketing data.

- Build high-throughput streaming and near-real-time pipelines with replay, idempotency, fault recovery, and correct handling of late or duplicate events.

- Define latency, freshness, completeness, quality, and reliability objectives.

- Create reusable, tenant-aware frameworks supporting multiple businesses and markets.

- Optimize Spark, Kafka, BigQuery, Dataproc, storage, and orchestration performance and cost.

- Strengthen data quality, lineage, observability, testing, deployment, and incident response.

- Lead domain design reviews, mentor engineers, and enable governed agentic workflows for discovery, automation, diagnostics, and onboarding across global advertising products and partner teams.

What you'll bring:

- Strong experience designing and operating billion-scale distributed data systems using Spark, Kafka, streaming, orchestration, and large-scale batch technologies.