SDET Sr Python Developer & Lead - Hybrid/Onsite at NTT DATA

Auburn Hills, Michigan, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, Airflow, CI/CD, Docker, Cloud Platforms, SQL, Unix/Linux, Git, TDD, Data Engineering, Mentorship, Troubleshooting, Collaboration, Documentation, Data Pipelines

Industry

IT Services and IT Consulting

Description

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. Minimum 5+ years of practical experience for the below-mentioned points 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). 3+ years of experience Unix/Linux: Strong command-line skills** in Unix-like environments. 3+ 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. 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 7+ years overall IT experience Experienced in waterfall, iterative, and agile methodologies A high tolerance for OpenShift, Cloudera, Tableau, Confluence, Jira, and other enterprise tools

Responsibilities

The role involves designing, developing, and optimizing scalable data pipelines and ensuring the reliability of complex workflows. Additionally, the candidate will lead and mentor junior engineers while collaborating with various teams to translate requirements into technical solutions.