Build scalable Python-based data pipelines and backend services for analytics workflows.
Design software systems using object-oriented programming and sound engineering practices.
Create and support platforms that allow analytics development, model training, and model deployment.
Implement and maintain CI/CD pipelines and infrastructure-as-code solutions for automated deployments.
Manage cloud and on-premises analytics environments, including AWS infrastructure and security controls.
Monitor, troubleshoot, and improve data pipelines, platform performance, and system reliability.
Support machine learning and fraud modeling workflows, including feature engineering and model deployment.
Implement new technologies, including AI-based solutions, to improve platform efficiency and stability.

3+ years of experience in data science, analytics, data engineering, or a related field.
Bachelor's or advanced degree in Statistics, Applied Mathematics, Econometrics, or another quantitative field; equivalent experience considered.
Experience developing applications and data pipelines using Python, including PySpark, Polars, NumPy, and Pandas.
Familiarity with Java and object-oriented programming concepts.
Experience building, deploying, and supporting production systems and data platforms.
Experience with AWS services such as EC2, EMR, and Airflow, including cloud security best practices.
Experience with machine learning workflows and analytics model development environments.
Experience with CI/CD processes, Infrastructure as Code, containerization tools, and UNIX/Linux environments.

Great compensation package and bonus plan.
Core benefits including medical, dental, vision, and matching 401K.
Flexible work environment, ability to work remote, hybrid or in-office.
Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.

Analytics Engineer

Key skills