Data Engineer

ABOUT REPAY

REPAY (“Realtime Electronic Payments” / NASDAQ TICKER: RPAY) is an established and fast-growing publicly traded financial technology and payment processing company headquartered in Atlanta, Georgia, with offices across the country. REPAY enables its customers to accept payments anytime, anywhere, and through any channel while providing a secure, seamless, and enjoyable payment experience for the end consumers. REPAY offers a comprehensive suite of electronic payment and funding solutions, including debit and credit card processing, ACH processing, Instant Funding, and electronic bill payment systems with full IVR, text, and mobile capabilities.

The scalability of its products allows merchants of all sizes to add an instant arsenal of intelligent payment technology solutions to their businesses without significant development costs or infrastructure investments.

REPAY is looking for a Data Engineer to join our growing team. The Data Engineer is responsible for designing, building, optimizing, and maintaining scalable cloud-based data infrastructure and data pipelines that enable reliable data processing, analytics, reporting, and business intelligence capabilities. This role focuses on developing production‑grade data pipelines, data models, and ETL/ELT processes using modern data engineering tools and platforms, including AWS, Databricks, PySpark, SQL, and Python.

The Data Engineer partners with BI, Product, Engineering, and client‑facing teams to ensure high-quality, well‑documented, and performance‑optimized data solutions that support business insights and operational decision‑making.

• Design, build, and maintain scalable, reliable cloud‑based data pipelines and data infrastructure.
• Deliver high‑quality data models and curated datasets that support analytics, reporting, and data‑driven decision‑making.
• Optimize Spark, PySpark, and SQL workloads to improve performance, reliability, cost efficiency, and scalability.
• Support production data pipelines through monitoring, troubleshooting, incident resolution, and continuous improvement.
• Implement data engineering standards, CI/CD practices, automated deployment processes, unit testing, and code quality expectations.
• Partner with BI, Product, Engineering, and client‑facing teams to translate business and reporting requirements into scalable data solutions.
• Document technical solutions, data flows, pipeline logic, and operational processes to support knowledge sharing and long‑term maintainability.
• Design, build, maintain, and optimize data pipelines using Python, SQL, PySpark, Databricks, and AWS‑based data services.
• Develop ETL/ELT processes that support data warehousing, analytics, reporting, and business intelligence use cases.
• Build and optimize Spark jobs, with a focus on performance, scalability, reliability, and efficient resource utilization.
• Design and implement data models for structured, semi‑structured, and No
  
  SQL data where applicable.
• Implement CI/CD practices, automated deployments, unit tests, and code quality standards for data engineering workflows.
• Monitor, troubleshoot, and support production data pipelines, resolving issues and recommending improvements.
• Collaborate with BI Analysts, Product, Engineering, Data, and client‑facing teams to understand requirements and support reporting needs.
• Document technical solutions, data flows, pipeline logic, and operational processes.
• Share technical knowledge through documentation, mentorship, and team knowledge‑sharing sessions.
• Stay current with advancements in data engineering, cloud platforms, Spark, Databricks, data warehousing, and analytics technologies.
• Participate in client‑facing design sessions, technical presentations, workshops, or training as needed.
• Other duties as assigned.

• Required Undergraduate or Masters’ degree in Computer Science, Statistics, or Analytics.
• Minimum of 3–5 years of experience in Data Engineering, preferably working with AWS‑based cloud data platforms.
• Hands‑on experience building, maintaining, and supporting cloud‑based data pipelines.
• Strong knowledge of PySpark, preferably on the Databricks platform.
• Hands‑on…