Data Engineer

REPAY•Atlanta, GA

39d

About The Position

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.

Requirements

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.

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.

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.