Responsibilities: Design, develop, and maintain Python-based applications and data processing pipelines for banking and financial services. Build and optimize ETL/ELT workflows using Azure Databricks, PySpark, and cloud-native components. Work closely with Quant, Risk, Treasury, and Operations teams to translate business requirements into scalable technical solutions. Develop APIs, automation tools, and reusable data frameworks to support enterprise-wide analytics. Implement data quality checks, data validation routines, and performance tuning strategies. Manage and deploy workloads in Azure using services such as Azure Data Factory, Azure Storage, Azure Functions, Key Vault, and CI/CD pipelines. Collaborate with on-site team members three times a week in New York/New Jersey for planning, code reviews, and architecture discussions. Ensure solutions meet security, compliance, and governance requirements common in highly regulated banking environments. Troubleshoot production issues, identify root causes, and provide timely fixes. Document technical designs, workflows, and operational procedures. Required Qualifications: 8+ years of hands-on experience as a Python Developer or Backend Engineer. Strong expertise with Python, including multiprocessing, OOP, and package development. Solid background in data engineering, distributed computing, and large-scale data pipelines. Strong SQL skills and experience with relational databases (PostgreSQL, SQL Server, Oracle, etc.). Banking or financial services experience—Risk, Treasury, Capital Markets, or Compliance preferred. Experience with API development (REST), microservices, and containerization (Docker/Kubernetes is a plus). Familiarity with security best practices in enterprise cloud environments. Excellent communication and collaboration skills; ability to work effectively in hybrid, on-site teams. Must be local to New York or New Jersey and able to commute onsite 3 days per week. No relocation candidates accepted. Preferred Qualifications: Experience with event-driven architectures (Kafka, Event Hubs). Knowledge of Pandas, NumPy, or other analytical libraries. Experience working directly with front-office, trading, or quant teams. Understanding of CI/CD, infrastructure-as-code (Terraform, ARM templates). Strong problem-solving and debugging skills in distributed systems.