Data Engineer / Data Modeler Payments Subsystem

Compunnel, Inc.; Atlanta (GA); On-site; USD 90,000 - 120,000; Full time

Job summary

A financial technology company in Atlanta is seeking an experienced Data Engineer / Data Modeler to focus on the payments subsystem. The role involves designing data pipelines and models for processing payments, ensuring data quality, and collaborating with various teams. The ideal candidate has at least 7 years of experience in data engineering, strong knowledge of payments domain concepts, and proficiency in tools like SQL and cloud platforms.

Qualifications

• Minimum 7 years of experience in data engineering and modeling within financial services.
• Strong knowledge of payments domain concepts.
• Hands-on experience with data modeling techniques.
• Proficiency in SQL and ETL tools.

Responsibilities

• Design and implement data models for the payments domain.
• Develop ETL/ELT pipelines from core banking systems.
• Ensure data quality and governance across payment processing flows.
• Build and optimize data warehouses on cloud platforms.

Skills

Data modeling

ETL development

SQL

Python

Real-time streaming technologies

Education

Bachelor’s degree in Computer Science, Information Systems, or related field

Tools

DataStage

Informatica

Talend

AWS Redshift

Azure Synapse

Snowflake

Databricks

Kafka

Kinesis

Docker

Kubernetes

Job description

Data Engineer / Data Modeler Payments Subsystem

Georgia, Atlanta

09/26/2025

Contract

Active

Job Description:

Job Summary

We are seeking an experienced Data Engineer / Data Modeler to join our banking technology team, with a focus on the payments subsystem.

This role involves designing, developing, and maintaining scalable data pipelines and models to support real-time and batch payment processing, settlement, reconciliation, and regulatory reporting.

The ideal candidate will have deep expertise in data modeling, ETL development, cloud data platforms, and modern data engineering practices.

Key Responsibilities

• Design and implement logical and physical data models for the payments domain, including merchant onboarding, transactions, clearing, settlement, funding, fraud/risk, and reporting.
• Develop ETL/ELT pipelines to integrate data from core banking systems, payment gateways, and external partners into enterprise data stores.
• Ensure data quality, lineage, and governance across payment processing flows.
• Build and optimize data warehouses and lakes on cloud platforms such as AWS or Azure.
• Collaborate with architects, developers, and business analysts to map payment business processes into data models and schemas.
• Implement real-time and batch data integration for use cases such as fraud detection, reconciliations, settlement reporting, and regulatory compliance.
• Monitor and tune pipelines for scalability, performance, and cost efficiency.
• Partner with security and compliance teams to enforce data privacy and regulatory requirements (e.g., PCI-DSS, SOX, AML/KYC).

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or a related field (or equivalent experience).
• Minimum 7 years of experience in data engineering and data modeling within financial services or banking.
• Strong knowledge of payments domain concepts including authorization, clearing, settlement, reconciliation, chargebacks, and fraud.
• Hands-on experience with data modeling techniques such as 3NF, star/snowflake schemas, and dimensional modeling.
• Proficiency in SQL and experience with ETL/ELT tools such as DataStage, Informatica, Talend, or dbt.
• Strong experience with cloud data platforms including AWS Redshift, Azure Synapse, Snowflake, or Databricks.
• Proficiency in Python for data processing and automation.
• Experience with real-time streaming technologies such as Kafka, Kinesis, or EventHub.
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines.

Preferred Qualifications

• Knowledge of regulatory and compliance requirements in payments and banking.
• Understanding of API-based integration for payment systems.
• Familiarity with containerization tools such as Docker and Kubernetes for deploying data services.