Python Technical Architect/ Lead

Location: Auburn Hills, MI- Onsite

Key Responsibilities: Data Engineering: Design, develop, and optimize scalable data pipelines using Python and PySpark for batch and streaming workloads. Workflow Orchestration: Build, schedule, and monitor complex workflows using Airflow, ensuring reliability and maintainability. CI/CD Pipeline Development: Architect and implement CI/CD pipelines for data engineering projects using GitHub, Docker, and cloud-native solutions. Testing & Quality: Apply test-driven development (TDD) practices and automate unit/integration tests for data pipelines. Secure Development: Implement secure coding best practices and design patterns throughout the development lifecycle. Collaboration: Work closely with Data Architects, QA teams, and business stakeholders to translate requirements into technical solutions. Documentation: Create and maintain technical documentation, including process/data flow diagrams and system design artifacts. Mentorship: Lead and mentor junior engineers, providing guidance on coding, testing, and deployment best practices. Troubleshooting: Analyze and resolve technical issues across the data stack, including pipeline failures and performance bottlenecks.

Technical Experience: Hands-on Data Engineering: Minimum 5+ years of practical experience building production-grade data pipelines using Python and PySpark. Airflow Expertise: Proven track record of designing, deploying, and managing Airflow DAGs in enterprise environments. CI/CD for Data Projects: Ability to build and maintain CI/CD pipelines for data engineering workflows, including automated testing and deployment. Cloud & Containers: Experience with containerization (Docker and cloud platforms (GCP) for data engineering workloads. Appreciation for twelve-factor design principles. Python Fluency: Ability to write object-oriented Python code manage dependencies, and follow industry best practices. Version Control: Proficiency with Git for source code management and collaboration (commits, branching, merging, GitHub/GitLab workflows).Unix/Linux: Strong command-line skills in Unix-like environments. SQL: Solid understanding of SQL for data ingestion and analysis. Collaborative Development: Comfortable with code reviews, pair programming and using remote collaboration tools effectively. Engineering Mindset: Writes code with an eye for maintainability and testability; excited to build production-grade software.

Education: Bachelor's or graduate degree in Computer Science, Data Analytics or related field, or equivalent work experience. A high tolerance for OpenShift, Cloudera, Tableau, Confluence, Jira, and other enterprise tools.