Data Engineer with Python

About this opportunity:

This position plays a crucial role in the development of Python-based solutions, their deployment within a Kubernetes-based environment, and ensuring the smooth data flow for our machine learning and data science initiatives. The ideal candidate will possess a strong foundation in Python programming, hands-on experience with ElasticSearch, Logstash, and Kibana (ELK), a solid grasp of fundamental Spark concepts, and familiarity with visualization tools such as Grafana and Kibana. Furthermore, a background in ML Ops and expertise in both machine learning model development and deployment will be highly advantageous.

What you will do:

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, data indexing, and real-time data visualization. Knowledge of OpenSearch and 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.

What you will Bring:

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 the implementation of 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.

Why join Ericsson?

At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply?

Click Here to find all you need to know about what our typical hiring process looks like.

Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: India (IN) || Bangalore

Req ID: 766745