Senior Data Manager - ETL/Python (3-5 yrs)

Job Title : Data Engineer

Experience : 3-6 Years

Location : Whitefield, Bangalore

Employment Type : Full-Time

Key Responsibilities :

1. Data Pipeline Development : Design, develop, and maintain efficient ETL pipelines to collect, transform, and load data.

2. Data Modeling : Develop Logical and Physical Data Models, including data warehouse designs, to support business requirements.

3. Monitoring and Maintenance : Implement monitoring and alerting systems to ensure the reliability of data pipelines. Perform routine maintenance and troubleshooting as needed.

4. Collaboration : Work closely with upstream and downstream teams to ensure end-to-end execution of data pipelines.

5. Optimization : Optimize data processing and storage solutions for scalability and efficiency when working with large volumes of data.

6. Development Support : Contribute to all phases of development, including design, implementation, and operation of production systems.

7. Code Quality : Write clean, maintainable, and efficient code. Demonstrate a solid understanding of Object-Oriented Design (OOD) principles and ensure coding best practices are followed.

8. Problem Solving : Handle ambiguous or undefined problems with a structured and analytical approach.

9. Data Security : Ensure adherence to data security and privacy principles in all aspects of data handling.

Required Skills and Qualifications :

1. Programming Expertise : Strong proficiency in Python for data manipulation and transformation.

2. Database Knowledge : In-depth understanding of either NoSQL or relational databases, with experience in data modeling.

3. Workflow Management : Hands-on experience with workflow management tools like Apache Airflow.

4. ETL Expertise : Proven experience building and maintaining ETL pipelines for large-scale data processing.

5. Design Principles : Strong grasp of Object-Oriented Design concepts and best coding practices.

6. Problem-Solving Skills : Ability to tackle undefined or ambiguous problems effectively.

7. Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for data storage and processing.

9. Experience with big data tools like Spark, Kafka, or Hadoop.

9. Knowledge of CI/CD pipelines and DevOps practices.