Senior Python Engineer - Big Data (TechFin) | Hybrid

Rose International; Tampa (FL); Hybrid; USD 177,000 - 187,000; Full time

Job summary

Rose International is seeking a Core Python Engineer with expertise in big data processing and experience in TechFin environments. The role focuses on building scalable data pipelines for credit risk analytics and regulatory reporting, using Python, Spark, and SQL with enterprise databases.

Strong CI/CD experience with GitHub, Tekton, and Harness is required. You will lead projects, collaborate with cross-functional teams, and contribute to the performance tuning of Spark jobs while leveraging

Qualifications

• 7+ years of software development experience with at least 5 years of hands-on Python and Apache Spark development in enterprise environments.
• 5+ years of experience with strong SQL skills with enterprise databases such as Oracle, PostgreSQL, SQL Server, or MySQL.
• 3+ years of experience leading/owning software development projects from beginning to end with multiple team members.

Responsibilities

• Design, develop, optimize, and maintain scalable data pipelines for processing and analyzing large scale financial data.
• Lead projects for developing batch pipeline processes for credit risk analytics including, but not limited to, stress loss (GSST, CCAR) and expected credit loss (CECL, IFRS9, ICAAP).
• Maintain code quality through high-efficiency testing, CI/CD pipelines (Tekton, Harness), and version control (GitHub).

Skills

Big Data

CI/CD tools

Hadoop

Hive

Python

Spark

SQL

Education

Bachelor’s Degree

Tools

GitHub

Tekton

Harness

Job description

Rose International is seeking a Core Python Engineer with expertise in big data processing and experience in TechFin environments. The role focuses on building scalable data pipelines for credit risk analytics and regulatory reporting, using Python, Spark, and SQL with enterprise databases.

Strong CI/CD experience with GitHub, Tekton, and Harness is required. You will lead projects, collaborate with cross-functional teams, and contribute to the performance tuning of Spark jobs while leveraging