Data Engineer - Cloud Pipelines & Analytics

REPAY Company; Tempe (AZ); Híbrido; USD 110 000 - 140 000; Tempo integral

Resumo da oferta

REPAY is seeking a Data Engineer to design, build, and optimize scalable cloud-based data pipelines using PySpark, SQL, and Python. You will model data, develop ETL/ELT processes, and partner with BI, Product, and Engineering teams to enable analytics and decision-making.

The role emphasizes production‑grade data infrastructure on AWS and Databricks, with CI/CD, monitoring, and best-practice data governance across datasets and pipelines.

Qualificações

• Undergraduate or graduate degree in CS, Statistics, or Analytics.
• 3–5 years of Data Engineering experience on AWS-based platforms.
• Strong PySpark experience, preferably on Databricks.
• Proficient in SQL and Python; data modeling and ETL/ELT concepts.
• Experience with CI/CD practices and production data pipelines.

Responsabilidades

• Design, build, and maintain scalable cloud-based data pipelines.
• Deliver high-quality data models and curated datasets for analytics.
• Optimize Spark, PySpark, and SQL workloads for performance and cost efficiency.
• Support production data pipelines through monitoring and troubleshooting.
• Document data flows, pipeline logic, and operational processes.
• Collaborate with BI, Product, Engineering, and client-facing teams.

Conhecimentos

PySpark

SQL

Python

Data modeling

Analytics

Formação académica

Bachelor's or Master's in Computer Science, Statistics, or Analytics

Ferramentas

Databricks

AWS

CI/CD

Kafka

MongoDB

Terraform

Power BI

Descrição da oferta de emprego

REPAY is seeking a Data Engineer to design, build, and optimize scalable cloud-based data pipelines using PySpark, SQL, and Python. You will model data, develop ETL/ELT processes, and partner with BI, Product, and Engineering teams to enable analytics and decision-making.

The role emphasizes production‑grade data infrastructure on AWS and Databricks, with CI/CD, monitoring, and best-practice data governance across datasets and pipelines.