Ericsson Hiring Data Engineer

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About this opportunity:

We are seeking a talented and dedicated Data Engineer to join our team at Ericsson. The successful candidate will be responsible for developing and delivering data solutions to fulfill our business needs and specifications. By creating and optimizing data pipelines and data models for our analytical solution’s data lake / data warehouses, you’ll be contributing to specific microservices we offer. The role also encompasses the responsibility for managing all data entering our data lake/DWH, ensuring the content, format, and integrity remain optimal throughout the data’s lifecycle.

Your Roles and Responsibilities (Data Engineer):

•    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.
•    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.

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