Senior Python Lead and Developer - Onsite

Req ID: 350358 

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

 

We are currently seeking a Senior Python Lead and Developer - Onsite to join our team in Auburn Hills, Michigan (US-MI), United States (US).

 

The Senior Data Engineer & Technical Lead will play a pivotal role in delivering major data engineering initiatives within the Data & Advanced Analytics space. This position requires hands-on expertise in building, deploying, and maintaining robust data pipelines using Python, PySpark, and Airflow, as well as designing and implementing CI/CD processes for data engineering projects.This will be a Technical Lead who can design (architect), develop, and lead a team.

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. 

 

Basic Qualifications:

Minimum 8-10 years of practical experience for the below-mentioned points

• Hands-on Data Engineering: 
• Minimum 8+ 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).
• 5+ years of experience Unix/Linux: Strong command-line skills** in Unix-like environments.
• 5+ years of experience 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.

Graduate degree in a related field, such as Computer Science or Data Analytics
Familiarity with Test-Driven Development (TDD)
Cross-Team Knowledge Sharing: Crosstrain team members outside the project team (e.g., operations support) for full knowledge coverage. Includes all above key responsibilities and skills, plus the following.

• Minimum of 10+ years overall IT experience
• Experienced in waterfall, it