Data Engineer

Summary

REPAY is a financial technology and payment processing company that provides electronic payment and funding solutions. The Data Engineer will design, build, optimize, and maintain cloud-based data infrastructure, data pipelines, data models, and ETL/ELT processes to support analytics, reporting, business intelligence, and operational decision-making.

Responsibilities

• 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 NoSQL 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

Skills

• 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 experience with Databricks
• Strong proficiency in SQL, including query optimization
• Strong proficiency in Python
• Strong knowledge of data modeling, data warehousing, ETL/ELT, and analytics concepts
• Experience with CI/CD practices, automated deployment processes, unit testing, and code quality standards
• Experience troubleshooting, monitoring, and supporting production data pipelines
• Experience documenting technical solutions, data flows, and pipeline logic
• Strong analytical and problem-solving skills, with the ability to translate business requirements into scalable data solutions
• Excellent written and verbal communication skills, including the ability to explain technical concepts to technical and non-technical stakeholders
• Ability to collaborate effectively across BI, Product, Engineering, Data, and client-facing teams
• Strong organizational skills and ability to manage multiple priorities in a fast-paced environment
• Proactive, ownership-oriented mindset with the ability to work independently and drive solutions from design through production support
• Professionalism and composure when supporting production issues or participating in client-facing discussions
• We are interested in every qualified candidate who is eligible to work in the United States
• This position is not eligible for hire in California
• Additionally, we are not able to sponsor visas
• Apache Kafka experience
• AWS Lambda experience
• AWS Glue experience
• MongoDB experience
• Experience with streaming technologies such as Kafka or Kinesis
• Terraform experience
• Familiarity with an analytics/visualization platform such as Power BI or Tableau

Qualifications

Must Haves

• 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 experience with Databricks
• Strong proficiency in SQL, including query optimization
• Strong proficiency in Python
• Strong knowledge of data modeling, data warehousing, ETL/ELT, and analytics concepts
• Experience with CI/CD practices, automated deployment processes, unit testing, and code quality standards
• Experience troubleshooting, monitoring, and supporting production data pipelines
• Experience documenting technical solutions, data flows, and pipeline logic
• Strong analytical and problem-solving skills, with the ability to translate business requirements into scalable data solutions
• Excellent written and verbal communication skills, including the ability to explain technical concepts to technical and non-technical stakeholders
• Ability to collaborate effectively across BI, Product, Engineering, Data, and client-facing teams
• Strong organizational skills and ability to manage multiple priorities in a fast-paced environment
• Proactive, ownership-oriented mindset with the ability to work independently and drive solutions from design through production support
• Professionalism and composure when supporting production issues or participating in client-facing discussions
• We are interested in every qualified candidate who is eligible to work in the United States
• This position is not eligible for hire in California
• Additionally, we are not able to sponsor visas

Nice to Haves

• Apache Kafka experience
• AWS Lambda experience
• AWS Glue experience
• MongoDB experience
• Experience with streaming technologies such as Kafka or Kinesis
• Terraform experience
• Familiarity with an analytics/visualization platform such as Power BI or Tableau

Benefits

• Business casual dress
• Great snacks & beverages
• Open-air collaborative team settings
• Continuing education, including professional conferences and events
• 100% coverage of employee healthcare premiums
• Free life insurance
• Free disability insurance
• Work-life balance resources
• All benefits go into effect day one
• 401(k)-employer match
• Employee Stock Purchase Plan
• Eligibility to participate in the Annual Bonus Program, with the bonus award reflecting excellent individual performance and goals achieved during the past year