Data Engineer for Sustainable Energy Solutions

5 days ago


Stockholm, Stockholm, Sweden Greenely Full time

Job Overview:

At Greenely, we are committed to creating the best customer experience for the modern energy consumer through technology. As a Digital Energy Provider, we help people reduce their electricity costs by optimizing their home and car's electricity usage with our smart app.

Key Responsibilities:

  1. Design and Implement Robust Data Architectures: Develop and implement data architecture designs that meet the needs of our users, ensuring data is reliable, secure, and scalable.
  2. Ensure Data Quality and Integrity: Guarantee that data is consistent, accurate, and available for analysis by stakeholders.
  3. Implement Machine Learning Model Management: Develop and implement systems for life-cycle management and version control of ML models, including training- and evaluation datasets.
  4. Setup Monitoring Systems: Establish processes to enable monitoring of key operational robustness metrics, ensuring problems are alerted and escalated in a timely manner.
  5. Develop Data Governance Frameworks: Create system support to track what data is stored where, for what purpose, and who has access to it, facilitating compliance with regulatory procedures and user requirements.
  6. Collaborate on Policies and Procedures: Work with technical leadership to develop and implement relevant policies and procedures to enable scalability, regulatory compliance, and security aspects.

Requirements:

  • Proven Expertise in Data Engineering: Experience designing and implementing scalable data architectures, including ETL pipelines and storage solutions, expertise in database technologies (SQL and NoSQL), data modeling, and performance optimization.
  • Strong Understanding of Data Governance: Strong understanding of data governance frameworks, data cataloging, and regulatory compliance, ability to implement robust security protocols to ensure data privacy and protection.
  • Hands-On Experience in Data Pipeline Optimization: Experience building and optimizing data workflows for both operational and analytical use cases, ability to ensure data reliability, consistency, and availability through proactive monitoring and troubleshooting.


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