Data Engineer

Position:

Job Description:

Key Responsibilities:

- Design, develop, and maintain data pipelines and architectures.

- Implement and optimize data integration and transformation processes.

- Collaborate with data scientists and machine learning engineers to integrate AI/ML models into data workflows.

- Utilize cloud platforms (AWS, GCP, Azure) to deploy and manage data solutions.

- Ensure high-quality data ingestion and processing through rigorous testing and validation.

- Participate in code reviews to maintain high code quality and standards.

- Troubleshoot and debug data engineering solutions, ensuring reliability and performance.

- Work with version control systems (preferably git) to manage and maintain codebases.

Mandatory Skills (Need Proficiency):

1. Experience: 5 to 10 years in software/data engineering.

2. Data Technologies: Proficiency in SQL, NoSQL databases (e.g., DynamoDB, MongoDB), ETL tools, and data warehousing solutions.

3. Programming Languages: Proficiency in Python is a must.

4. Cloud Platforms: Azure, AWS (e.g., EC2, S3, RDS) or GCP.

5. Visualization Tools: Experience with data visualization tools (e.g., Tableau, Power BI, Looker).

6. Data Governance: Knowledge of data governance and security practices.

7. CI/CD: Experience with DevOps practices, including CI/CD pipelines and containerization (Docker, Kubernetes).

8. Communication Skills: Excellent verbal and written communication skills in English.

9. Agile Methodologies: Experience working in Agile development environments.

10. AI/ML Awareness: Understanding of AI and ML concepts, frameworks (e.g., TensorFlow, PyTorch), and practical applications.0

11. Generative AI Awareness: Familiarity with Generative AI technologies and their potential use cases.

Good to Have Skills:

1. APIs: Understanding of RESTful APIs and their integration into data workflows.

2. Advanced Analytics: Knowledge of advanced analytics and statistical techniques.

3. Microservices: Familiarity with microservices architecture and its implementation.

4. Data Lakes: Understanding of data lakes and their role in modern data architecture.

5. Scripting: Proficiency in shell scripting and automation.

6. AI/ML Deployment: Experience working with and deploying AI/ML models in production environments.

7. Big Data Tools: Experience with Hadoop, Spark, Kafka, and other big data technologies.

Qualifications:

- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

- Certifications: Relevant certifications in data engineering, cloud platforms, or AI/ML are a plus.

Location:

Time Type:

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