Recovery Data Expert Data Engineer
Nordea Näytä kaikki työpaikat
- Helsinki
- Vakituinen
- Täyspäiväinen
- Work closely with stakeholders in Business to turn their business problems into fit for purpose data solutions
- Get a deep understanding of the Recovery data domain to take ownership of designing and building the Recovery data model
- Take ownership of delivery from requirements to production with long term sustainability in mind
- Take ownership of designing and building data pipelines
- Design and develop technical enablers with a focus on automating everything
- Act as guide to technical team and ensuring development standards, solid engineering practices are followed
- Set a high standard for what you deliver, prioritizing long-term reliability, stability, and maintainability over quick fixes
- Stay current with AI assisted development and adopt AI to increase efficiency, maintainability, and reliability
- Don't wait for detailed specifications - you proactively understand the business domain and ask the right questions to clarify the requirements
- Take ownership of the full lifecycle - from design to implementation and testing - and stand by the solutions you deliver
- Understand what good looks like in data warehousing and build accordingly
- Are interested in developing deep domain knowledge
- Care about long-term maintainability over short-term fixes
- Use AI as a tool to improve productivity, quality and speed
- Degree in Computer Science, Computer Engineering, information technology, data engineering or relevant technical field
- Strong experience in data engineering and data warehousing, building large-scale data solutions
- Comfortable taking full ownership from requirements to production
- Strong SQL and Python skills
- Strong Experience with modern data platforms (Snowflake, dbt, Airflow)
- Excellent in data modelling
- Experience with enterprise API integration
- Solid engineering practices (git, CI/CD, automated testing)
- Have experience with AI and how AI can support development and operations of large-scale data solutions