Ericsson Recruitment 2024 – Data Engineer/Associate Engineer Trainee
Ericsson Recruitment 2024 :
Ericsson Recruitment 2024: Ericsson is scheduled to hire for the Data Engineer/Associate Engineer Trainee role. Candidates with B.E/B.Tech qualifications are eligible to apply for this role. Candidates looking for a job in Noida/Bangalore can utilize this opportunity. Interested and eligible candidates can apply online as soon as possible. The detailed eligibility criteria and application process are given below.
Ericsson Recruitment 2024 Details :
| Designation | Data Engineer |
| Eligibility | B.E/B.Tech |
| Job Location | Bangalore |
| Experience : | Freshers |
Telegram |
|
Get Daily Job Notification Join us on Telegram. To Join: Click here
Detailed Eligibility
- **Data Engineer:** B.E/B.Tech qualifications
Roles & Responsibilities
- **Python Development:** Write clean, efficient, and maintainable Python code to support data engineering tasks, including data collection, transformation, and integration with machine learning models.
- **Data Pipeline Development:** Design, develop, and maintain robust data pipelines that efficiently gather, process, and transform data from various sources into a format suitable for machine learning and data science tasks using ELK stack, Python and other leading technologies.
- **Spark Knowledge:** Apply basic Spark concepts for distributed data processing when necessary, optimizing data workflows for performance and scalability.
- **ELK Integration:** Utilize ElasticSearch, Logstash, and Kibana (ELK) for data management, indexing, and real-time data visualization. Knowledge of OpenSearch and the related stack would be beneficial.
- **Grafana and Kibana:** Create and manage dashboards and visualizations using Grafana and Kibana to provide real-time insights into data and system performance.
- **Kubernetes Deployment:** Deploy data engineering solutions and machine learning models to a Kubernetes-based environment, ensuring security, scalability, reliability, and high availability.
- **Machine Learning Model Development:** Collaborate with data scientists to develop and implement machine learning models, ensuring they meet performance and accuracy requirements.
- **Model Deployment and Monitoring:** Deploy machine learning models and implement monitoring solutions to track model performance, drift, and health.
- **Data Quality and Governance:** Implement data quality checks and data governance practices to ensure data accuracy, consistency, and compliance with data privacy regulations.
- **MLOps (Added Advantage):** Contribute to implementing MLOps practices, including model deployment, monitoring, and automation of machine learning workflows.
- **Documentation:** Maintain clear and comprehensive documentation for data engineering processes, ELK configurations, machine learning models, visualizations, and deployments.