Data Engineer I

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 required.
• 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.
• 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

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.