Python Operations Engineer, Development (52352)

Citrin Cooperman offers a dynamic work environment, fostering professional growth and collaboration. We’re continuously seeking talented individuals who bring a problem-solving mindset, fresh perspectives, and sharp technical expertise. We know you have choices, so our team of collaborative, innovative professionals are ready to support your professional development. At Citrin Cooperman, we offer competitive compensation and benefits and most importantly, the flexibility to manage your personal and professional life to focus on what matters most to you!

We are seeking a Python Operations Engineer, Development, to join our Development team within the Information Technology department. While our Data Engineers focus on core pipeline architecture, we’re seeking a versatile Python Engineer to serve as the “utility player” for the team, tackling complex data parsing, custom integrations, and operational automation.

In this highly dynamic role, you’ll be the primary developer who bridges the gaps between legacy systems, third-party APIs, and our modern data estate. You’ll write robust, production-grade Python code to handle the edge cases that standard data integration tools (like Azure Data Factory) can’t easily resolve. Whether you’re building custom API extractors, writing PySpark scripts to flatten deeply nested data structures, or automating routine infrastructure tasks alongside our Cloud Engineers, you’ll be the team’s go-to problem solver. The ideal candidate is a scrappy, adaptable coder who loves diving into messy data problems and takes pride in writing clean, efficient, and well-documented scripts.

Responsibilities are, but not limited to:

• Custom API Integrations: Design, develop, and maintain robust Python scripts to ingest data from complex, rate-limited, or poorly documented third-party APIs that lack native Fabric/Azure connectors.
• Complex Data Parsing: Write bespoke code to extract, parse, and flatten unstructured or semi-structured data formats (deeply nested JSON, XML, messy flat files, or logs) before handing the data off to the Data Engineers for Medallion layer modeling.
• Operational Automation: Partner closely with Database Administrators and Cloud Engineers to automate routine infrastructure, monitoring, and maintenance tasks using Python (e.g., interacting with Azure Resource Manager APIs or automating alert remediations).
• PySpark Development: Support the core Data Engineering team by writing and optimizing complex PySpark transformations within Microsoft Fabric notebooks for large-scale data processing.
• Internal Tooling: Build lightweight internal tools, Command Line Interfaces (CLIs), or microservices to improve operational efficiency and quality-of-life for the broader Data Operations team.
• Code Quality & CI/CD: Ensure all custom scripts are version-controlled via Git, rigorously tested, and seamlessly integrated into the team’s automated deployment pipelines.