Data Architect (Phoenix)

The Senior Data Engineer & Test in Phoenix 85029 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

Key Responsibilities

1. Data Engineering : Design, develop, and optimize scalable data pipelines using Python and PySpark for batch and streaming workloads.

2. Workflow Orchestration : Build, schedule, and monitor complex workflows using Airflow, ensuring reliability and maintainability.

3. CI / CD Pipeline Development : Architect and implement CI / CD pipelines for data engineering projects using GitHub, Docker, and cloud-native solutions.

4. Testing & Quality : Apply test-driven development (TDD) practices and automate unit / integration tests for data pipelines.

5. Secure Development : Implement secure coding best practices and design patterns throughout the development lifecycle.

6. Collaboration : Work closely with Data Architects, QA teams, and business stakeholders to translate requirements into technical solutions.

7. Documentation : Create and maintain technical documentation, including process / data flow diagrams and system design artifacts.

8. Mentorship : Lead and mentor junior engineers, providing guidance on coding, testing, and deployment best practices.

9. Troubleshooting : Analyze and resolve technical issues across the data stack, including pipeline failures and performance bottlenecks.

Cross-Team Knowledge Sharing : Cross-train team members outside the project team (e.g., operations support) for full knowledge coverage. Includes all above skills, plus the following;

Minimum of 10+ years overall IT experience

Experienced in waterfall, iterative, and agile methodologies

Technical Requirment :

1. Hands-on Data Engineering : Minimum 5+ yearsof practical experience building production-grade data pipelines using Python and PySpark.

2. Airflow Expertise : Proven track record of designing, deploying, and managing Airflow DAGs in enterprise environments.

3. CI / CD for Data Projects : Ability to build and maintain CI / CD pipelinesfor data engineering workflows, including automated testing and deployment

4. Cloud & Containers : Experience with containerization (Docker and cloud platforms (GCP) for data engineering workloads. Appreciation for twelve-factor design principles

5. Python Fluency : Ability to write object-oriented Python code manage dependencies, and follow industry best practices

6. Version Control : Proficiency with

• Git
• for source code management and collaboration (commits, branching, merging, GitHub / GitLab workflows).

7. Unix / Linux : Strong command-line skills

8. SQL : Solid understanding of SQL for data ingestion and analysis.

9. Collaborative Development : Comfortable with code reviews, pair programming and usingremote collaboration tools effectively.

10. Engineering Mindset : Writes code with an eye for maintainability and testability; excited to build production-grade software

11. Education : Bachelors or graduate degree in Computer Science, Data Analytics or related field, or equivalent work experience.