Skip to main content

mthree Recruiting Portal

Python Developer

New York, NY$85k–$120kmidAdded today

About this role

mthree seeks an intermediate Python developer for a financial services client to design and maintain scalable data pipelines, handling ETL processes and large datasets while collaborating with cross-functional teams in a fast-paced environment.

What you'll do

  • Design and develop scalable data pipelines using Python for ETL processes
  • Optimize data workflows to handle large datasets efficiently and ensure data quality
  • Write clean, maintainable code and develop APIs with proper documentation
  • Troubleshoot, debug, and resolve performance issues and data anomalies
  • Collaborate with cross-functional teams on technical solutions and participate in code reviews
  • Manage databases and implement data storage solutions using appropriate technologies

What they're looking for

  • Python with object-oriented programming principles
  • ETL tools (Apache Airflow, Apache Kafka)
  • SQL and NoSQL databases (PostgreSQL, MySQL, MongoDB)
  • Data libraries (NumPy, Pandas, Django, Flask)
  • Cloud platforms (AWS, Azure, GCP) and orchestration tools (Kubernetes, Terraform)
  • CI/CD tools (Git, Jenkins, GitHub)
  • Linux administration
  • Data structures and algorithms
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

mthree Recruiting Portal

mthree Recruiting Portal connects recent graduates with entry-level technology roles at leading financial services and enterprise organizations through its Alumni program. The company specializes in recruiting, training, and placing new talent in Production Support and Site Reliability Engineer positions, with a focus on investment banking and other industries.

Website
mthree.com
View all jobs at mthree Recruiting Portal

Likely interview questions

  • Describe your experience designing and implementing ETL pipelines in Python—what challenges did you face and how did you optimize performance?
  • How have you handled data quality issues or anomalies in a production environment?