SENIOR, DATA ENGINEER

Design, build, and maintain scalable batch, streaming, and near-real-time data pipelines to support post-payment audit, payment integrity, supplier reconciliation, and finance analytics workflows.

Create curated datasets, semantic models, and feature-ready tables from complex payment and supplier data sources to enable audit and machine learning use cases.

Implement data quality, observability, and GenAI data engineering patterns; optimize pipelines for reliability, performance, and cost; and collaborate with cross-functional teams to operationalize data solutions.

6+ years of relevant experience in data engineering with a Bachelor's degree (or equivalent advanced degree with reduced experience requirement).

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

Experience with DBT or similar frameworks, Airflow or orchestration tools, Kafka or event streaming platforms, and cloud/enterprise data platforms like BigQuery, Hive, or Spark.

Work Experience Required: 6 to 10 years relevant experience explicitly mentioned.

Experienced in designing and supporting production batch and streaming ELT/ETL data pipelines and lakehouse data products with formats like Apache Hudi, Iceberg, or Delta Lake.

Skilled at operating across complex, messy enterprise data from multiple systems, applying data quality, anomaly detection, and reconciliation controls in highly regulated finance and audit environments.

Able to integrate advanced GenAI data engineering patterns and partner closely with multi-disciplinary teams including Finance, Audit, Product, and Data Science to deliver trusted, operational data services.