Python Data Developer

Overview The Python Data Developer will design, develop, and maintain enterprise-grade data solutions that support regulatory and risk reporting requirements. This includes analyzing enterprise data and building Python-based microservices and Flask APIs for data movement, implementing business and computational rules, and constructing data models in PostgreSQL to support Enterprise Risk reporting ________________________________________ A. Key Responsibilities 1. Data Development (Python) • Strong SQL and data analysis skills. • Proficiency with PostgreSQL schema design, stored procedures, query optimization. • Expertise in ETL/ELT pipelines, data transformation, and rule-based data computation. • Hands-on experience building Python microservices (Flask preferred). • Build and maintain Python-based microservices to orchestrate and automate ELT/ETL workflows. • Develop Flask-based REST APIs to extract, transform, and deliver data between Snowflake, ARK databases, and downstream systems. • Design clear, scalable JSON schemas and structure data consistently for effective data management. • Develop Python scripts to create, parse, transform, and manage JSON data efficiently. • Convert and map data from multiple sources (databases, APIs, files) into standardized JSON formats and integrate with systems. • Ensure data accuracy through validation, handle errors, and optimize processing for large-scale datasets. 2. API & Microservices Development. • Design, implement, and deploy Python microservices to support data ingestion and enrichment. • Build secure Flask APIs to expose and consume data services for ARK and reporting systems. • Implement authentication, authorization, error handling, and logging within APIs and services. • Integrate CI/CD pipelines for automated build, test, and deployment. 3. Data Modeling & Reporting Layer Engineering (Power BI) • Design and build PostgreSQL data models optimized for analytics and regulatory reporting. • Create schemas, tables, stored procedures, indexes, and reporting-optimized structures. • Support dashboards and reporting modules used for Enterprise Risk reporting to the FED. • Develop computation logic for enterprise risk metrics, aggregation layers, time-series calculations, and regulatory formulas. • Understand the business requirements and translate them into a reporting data model suitable for Power BI. • Write optimized PostgreSQL queries, views, or stored logic to build a complex dataset for efficient report consumption. • Build a clean semantic model in Power BI with proper relationships, hierarchies, and DAX measures. • Ensure data quality, validate business rules, and manage complex joins in the dataset. • Optimize report performance through query tuning, model simplification, and efficient PBI design patterns. • Coordinate data refresh scheduling, troubleshoot errors, and ensure the reports run from end to end. 4. AI Tools Utilization & Leveraging AI Platforms • Leverage AI tools (Claude, Git Duo, Copilot, etc.) to accelerate solution design, documentation, and code generation. • Use AI-assisted data analysis to explore datasets, identify patterns, and derive insights that support reporting needs. • Develop and refine prompts to ensure accurate outputs, and validate AI-generated content for quality, reliability, and compliance. • Integrate AI-assisted workflows into development processes (e.g., code reviews, testing, optimization, debugging). ________________________________________ B. Other Critical Skills • Collaborate closely with architects, business analysts, QA teams. • Lead technical discussions around pipeline design, system integration, API frameworks, and data modeling. • Support code reviews, peer collaboration, and best practice adoption across teams. • Experience with data governance, metadata management, and enterprise data quality frameworks. • Effective communication, documentation, and analytical skills. • Preferably to have experience in Node.js and Angular