Data Engineer- Full Time Opportunity, NOT C2C at Codoxo

Duluth, Georgia, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Sep, 26

Salary

0.0

Posted On

19 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, SQL, AWS Glue, Apache Spark, Apache Airflow, PostgreSQL, ETL/ELT, AWS EC2, AWS S3, Linux, Shell Scripting, Data Pipeline Design, Data Validation, Dimensional Modeling, CI/CD

Industry

IT Services and IT Consulting

Description

PLEASE NOTE BEFORE APPLYING: CODOXO IS NOT ABLE TO OFFER SPONSORSHIP OR ACCOMMODATE ANY CANDIDATES THAT ARE CURRENTLY BEING SPONSORED NOW OR IN THE FUTURE This is for a Full Time Role with Codoxo, NOT C2C Of the $3.8T we spend on healthcare in the United States annually, about a third of it is estimated to be lost due to waste, fraud and abuse. Codoxo is the premier provider of artificial intelligence-driven solutions and services that help healthcare companies and agencies proactively detect and reduce risks from fraud, waste, and abuse and ensure payment integrity. Codoxo helps clients manage costs across network management, clinical care, provider coding and billing, payment integrity, and special investigation units. Our software-as-a service applications are built on our proven Forensic AI Engine, which uses patented AI-based technology to identify problems and suspicious behavior far faster and earlier than traditional techniques. We are venture backed by some of the top investors in the country, with strong financials, and remain one of the fastest growing healthcare AI companies in the industry. Position Summary: The Data Engineer supports the design, development, and maintenance of scalable data pipelines that power analytics, reporting, and machine learning initiatives. Working under the guidance of senior engineers, this role contributes to building reliable ETL workflows, optimizing database performance, and integrating structured and unstructured data sources. This position partners closely with data scientists, analysts, and cross-functional stakeholders to ensure timely, accurate, and secure data delivery. By strengthening foundational data infrastructure, the Junior Data Engineer helps advance analytics maturity, enable AI initiatives, and promote data-driven decision-making across the organization. The role consistently leverages AI tools to enhance productivity, code quality, and solution effectiveness. Key Responsibilities: * Assist in designing, building, and maintaining scalable ETL/ELT data pipelines. * Develop and optimize batch and streaming workflows using tools such as AWS Glue, Spark, and Airflow. * Support data integration across multiple structured and unstructured data sources. * Write clean, efficient, and maintainable code in Python, PySpark and SQL. * Monitor, troubleshoot, and improve pipeline reliability and performance. * Optimize database performance, particularly in PostgreSQL and cloud-based environments. * Maintain and support AWS-based infrastructure (EC2, S3, Glue, etc.). * Implement data validation, quality checks, and monitoring processes. * Ensure compliance with data governance, security, and regulatory standards. * Collaborate with data scientists and analysts to translate data requirements into scalable engineering solutions. * Document data flows, architecture decisions, and technical processes. * Use AI-assisted development tools to improve speed, testing coverage, and code quality. Qualifications: * Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field (or equivalent practical experience). * 2+ years of experience in data engineering, software engineering, or related technical roles (internships included). * Proficiency in Python, PySpark and SQL. * Familiarity with ETL/ELT concepts and data pipeline architecture. * Experience working with relational databases such as PostgreSQL. * Basic understanding of cloud computing concepts, preferably AWS. * Exposure to distributed data processing frameworks such as Spark. * Experience working in Linux environments and basic shell scripting. * Strong analytical and problem-solving skills. * Ability to work collaboratively in a team environment under mentorship. * Strong written and verbal communication skills. Preferred Qualifications * Experience working with medical claims data strongly preferred. * Hands-on experience with AWS services such as EC2, S3, Glue, and IAM. * Experience with workflow orchestration tools such as Apache Airflow. * Exposure to data warehousing concepts and dimensional modeling. * Familiarity with CI/CD pipelines and version control (e.g., Git). * Understanding of data security, governance, and compliance best practices. * Experience supporting machine learning pipelines or analytics platforms. * Demonstrated use of AI tools (e.g., code assistants, automation platforms) to improve development efficiency. * Physical Requirements: Work is performed in an office environment (either in our office or work-from home) and requires the ability to work on a computer, operate standard office equipment, and work at a desk. Accessibility Notice: If you need reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to careers@codoxo.com with the subject Benefits for You * Health, Dental, and Vision insurance with 100% employee premium coverage (Starts Day 1) * Unlimited PTO * Annual Professional Development stipend * Annual home office stipend * 401K Match (after 90 days) We are an Equal Opportunity Employer: Codoxo provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment.

Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines to support analytics and machine learning initiatives. Collaborate with data scientists and analysts to optimize database performance and ensure secure data delivery.