Data Engineer

ABOUT THE ROLE

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.

ROLES & 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.

QUALIFICATIONS

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 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-fac