QUALIFICATIONS EXPERIENCE

• Bachelor’s or Master’s degree in information technology, data science, scientific computing, engineering or related field.
• Technical expertise in data management, such as:
• working with and designing databases,
• proficiency with AWS Redshift and other AWS data services,
• hands-on experience with data modeling and transformation (such as dbt),
• strong knowledge of SQL and Python for data manipulation and scripting,
• familiarity with ETL/ELT processes and modern data stack tools.
• Strong knowledge and practical skills in software development (version control, testing, CI/CD).
• Excellent communication and interpersonal skills.
• Domain knowledge in biological sciences, biotech, business analytics.
• Ability to think strategically and solve complex data challenges.
  Does this sound like you? Then we would love to hear from you! Please apply using the provided link and form, attach your CV and your cover letter, outlining why you feel motivated and qualified for the role

As a Data Engineer at Nuritas, you will be at the forefront of our data strategy, helping to design, build, and maintain robust data infrastructure and pipelines. You’ll collaborate with teams across Nuritas to transform raw data into actionable insights that support scientific discovery, operational efficiency, and business growth to:

1. Data Infrastructure Development:

• Design, implement, and manage scalable data pipelines and workflows.

1. Cross-Functional Collaboration:

• Partner with teams across the company—science, machine learning, project management, commercial, and business operations—to understand their data needs and develop solutions tailored to their requirements.

1. Data Integration and Transformation:

• Consolidate data from multiple sources, ensuring it is clean, reliable, and optimized for use in analytics and reporting.

1. Automation and Optimization:

• Automate manual data processes, improve existing pipelines, and enhance data quality and performance.

1. Data Products Development:

• Build and maintain reusable data models, metrics, and tools that empower teams to make data-driven decisions.

1. Data Governance:

• Implement and maintain best practices for data security, quality, and compliance, ensuring that sensitive data is handled responsibly.

Our data infrastructure is diverse including both in-house databases and services as well as cloud-hosted warehouses (AWS Redshift) and workflows and several SaaS products.