Data Engineer at Softhouse Mälardalen
Workplace: Stockholm, Sverige
Expires: December 19, 2025
As a Data Engineer at Softhouse, you will develop and maintain data solutions enabling clients to make data-driven decisions. You will build pipelines, data warehouse solutions, and applications for data processing with a focus on scalability, performance, and usability. You will also create or integrate technical solutions for ETL and data mining and contribute to analysis and machine learning work.
Main requirements:
- Over 3 years of experience in similar roles related to Data Engineering, Data Science, and/or analytics.
- Strong proficiency in SQL and knowledge of at least one: Python, Node.js, or Java.
- Experience working with one or more cloud platforms such as AWS, GCP, Azure, Snowflake or equivalents.
- Experience taking responsibility for agile projects end-to-end and creating creative solutions.
- Ability to communicate fluently in both Swedish and English.
Responsibilities:
- Develop and maintain scalable data pipelines and data warehouse solutions.
- Develop applications for data processing focusing on scalability, performance, and usability.
- Create or integrate technical ETL and data mining solutions.
- Contribute to analytics and machine learning initiatives.
- Collaborate closely with a dedicated team to continuously improve work approaches.
Required hard skills:
- SQL
- Python or Node.js or Java
- Cloud platforms (AWS, GCP, Azure, Snowflake or equivalents)
- Agile project management and delivery
Recommended hard skills:
- MLOps experience (e.g., ML Flow or similar)
- Knowledge of Data Mesh and Data Products concepts
- Experience with big data technologies such as Hadoop, Kafka, or Spark
Soft skills:
- Curiosity
- Team player
- Problem solving and creativity
- Good communication skills
Coding languages:
- Python
- Node.js
- Java
- SQL
Frameworks:
- ML Flow (recommended)
Operating systems:
- Not explicitly specified
Natural languages:
- Swedish (Proficient)
- English (Proficient)
Cultural skills:
- Collaborative teamwork
- Openness and knowledge sharing
- Work-life balance focus
- Engagement in company values and social activities
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