SENIOR, DATA ENGINEER

Position Summary...

What you'll do...

Skillset: Python, PySpark, SQL Experience range: 6 to 10 years About the Team Walmart’s Corporate Tech and Services is a powerhouse of several exceptional teams delivering world-class technology solutions and services making a profound impact at every level of Walmart.

As a key part of Walmart Global Tech, our teams set the bar for operational excellence and leverage emerging technology to support millions of customers, associates, and stakeholders worldwide.

Each time an associate turns on their laptop, a customer makes a purchase, a new supplier is onboarded, the company closes the books, physical and legal risk is avoided, and when we pay our associates consistently and accurately, that is Corporate Tech.

Joining this team means embarking on a journey of limitless growth, relentless innovation, and the chance to set new industry standards that shape the future of Walmart.

Position Summary As a Senior Data Engineer on the Post Payment Audit team, you will design, build, and operate scalable data products that improve payment accuracy, identify overpayment and reconciliation opportunities, and help recover value across supplier payment workflows.

You will partner with Finance, Audit, Operations, Product, Data Science, and Engineering teams to transform complex invoice, purchase order, receiving, supplier, claims, pricing, and payment data into reliable pipelines, curated datasets, and production-ready analytics foundations.

This role is ideal for someone who combines deep data engineering expertise with strong business problem-solving and practical GenAI enablement skills.

You should be comfortable working across messy enterprise data, designing resilient batch, streaming, and CDC workflows, implementing data quality and observability patterns, managing lakehouse-style data products, supporting analytics and machine learning use cases, and making trusted data easier for audit teams and stakeholders to use.

What You Will Do: • Design, build, and maintain scalable batch, streaming, and near-real-time data pipelines that support post-payment audit, payment integrity, supplier reconciliation, recovery operations, and finance analytics workflows.

•Create reliable curated datasets, semantic data models, feature-ready tables, and reusable data products from invoice, purchase order, receiving, supplier, claims, pricing, and payment systems.

•Develop robust ELT/ETL and streaming workflows using SQL, Python, PySpark, Apache Flink, DBT, orchestration tools, event streaming platforms, and cloud data platforms, with a focus on maintainability, performance, and cost efficiency.

•Build streaming and change data capture pipelines that support event-time processing, late-arriving data, replayability, idempotency, backfills, and reliable recovery from failures.

•Design and manage lakehouse data products using table formats such as Apache Hudi, Apache Iceberg, or Delta Lake to support ACID transactions, incremental reads, upserts, time travel, and efficient auditability.

•Implement data quality checks, reconciliation controls, schema validation, lineage, monitoring, alerting, and observability practices to ensure trusted and repeatable data outputs.

•Partner with audit operations, finance stakeholders, product managers, data scientists, analysts, and engineers to turn business requirements into production-grade data solutions.

•Support anomaly detection, exception prioritization, audit rules, ML scoring, and GenAI workflows by delivering high-quality source data, feature engineering pipelines, and operational datasets.

•Build data services and automation patterns that help audit teams summarize evidence, triage exceptions, generate investigation context, and reduce repetitive manual review.

•Apply practical GenAI data engineering patterns such as retrieval-ready document stores, embeddings pipelines, vector database integrations, metadata enrichment, and governed knowledge retrieval.

•Optimize large-scale batch and streaming data processing jobs for reliability, freshness, latency, state management, compute efficiency, and downstream usability across enterprise data platforms.

•Perform root-cause analysis on data discrepancies, pipeline failures, reconciliation mismatches, and source-system issues across large and imperfect datasets.

What You Will Bring: • Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Engineering, Analytics, Mathematics, Statistics, or a related field with 6+ years of relevant experience; or Master’s degree with 4+ years of relevant experience; or PhD with 3+ years of relevant experience.

Strong hands-on experience with SQL, Python, PySpark, Apache Flink or equivalent stream processing frameworks, and large-scale distributed data processing.

Experience with DBT or equivalent transformation frameworks, orchestration tools such as Airflow, event streaming platforms such as Kafka or equivalent, and cloud or enterprise data platforms such as BigQuery, Hive, Spark , or similar technologies.

•Experience designing, building, deploying, and supporting production batch and streaming data pipelines, ELT/ETL workflows, data models, and reusable data products.

•Strong understanding of data modeling, partitioning, performance tuning, incremental processing, CDC, schema evolution, data contracts, metadata management, lineage, and lakehouse table formats such as Apache Hudi, Apache Iceberg, or Delta Lake.

•Experience implementing data quality checks, reconciliation logic, validation frameworks, observability, monitoring, alerting, and incident resolution for production batch and streaming pipelines.

•Experience building streaming data solutions with concepts such as event-time processing, stateful transformations, checkpointing, watermarking, exactly-once or effectively-once processing, and replay/backfill strategies.

•Experience working with large structured and semi-structured datasets from multiple source sys