Data Engineer (Hybrid - Atlanta, GA)

Purchasing Power•Atlanta, GA

8d•Hybrid

About The Position

Purchasing Power is a leading employee purchase program that helps people buy the products and services they need through the convenience of payroll deduction. As part of PROG Holdings (NYSE: PRG), a FinTech holding company with more than 20 years of experience, we’re focused on building simple, responsible financial solutions that put people first. PROG Holdings’ portfolio includes Progressive Leasing, a market leader in lease‑to‑own solutions, Purchasing Power, and Four, a Buy Now Pay Later (BNPL) platform — all united by a commitment to innovation, access, and meaningful impact. We’re looking for a Data Engineer to support the development and operation of our enterprise data platform. This role is highly tactical and execution‑focused, contributing directly to the reliability, scalability, and performance of our AWS‑based data ecosystem. You’ll work closely with the Senior Director of Data Engineering and platform team to design, build, and maintain streaming and batch data pipelines, supporting real‑time ingestion, transformation, and delivery into analytics and reporting systems. This is a great opportunity for someone who enjoys working in distributed systems and wants to have a direct impact on how data is delivered across the business.

Requirements

3–5+ years of experience in data engineering or distributed data systems
Hands‑on experience building and maintaining pipelines using Spark / PySpark
Experience working with Kafka or other streaming platforms
Strong SQL skills and experience with data transformation
Experience with data orchestration tools such as Apache Airflow
Familiarity with AWS‑based data environments
Strong troubleshooting and debugging skills for data pipelines
Curiosity, adaptability, and comfort working in evolving technical environments

Responsibilities

Data Pipeline Development
Develop and maintain streaming and batch data pipelines using Kafka, Spark, and related technologies
Implement ingestion pipelines using Kafka Connect and CDC patterns
Build transformation and enrichment processes using Spark and PySpark
Ensure reliable data delivery to downstream platforms including Greenplum, Elastic, S3, and analytics tools
Streaming Platform Support
Support Kafka topic configuration, partitioning strategies, and consumer group management
Assist with schema registration and schema evolution practices
Troubleshoot streaming pipeline issues and optimize performance
Help maintain data integrity and reliability across distributed systems
Data Processing & Transformation
Implement business logic and transformations within Spark processing frameworks
Develop and maintain Airflow DAGs (or equivalent orchestration workflows)
Build lookup and enrichment logic for streaming and batch pipelines
Ensure data quality through validation, monitoring, and alerting practices
Platform Operations
Monitor pipeline performance, failures, and data latency
Assist with observability and monitoring using tools such as Elastic and Kibana
Support incident resolution and root‑cause analysis for data pipeline issues
Contribute to system performance tuning and platform optimization
Collaboration
Work closely with the Data Engineering Manager to implement platform architecture
Partner with analytics and reporting teams to support BI and data delivery needs
Assist with evolving platform integrations and modernization initiatives