The Data & Analytics Engineering department brings together specialists in data infrastructure, analytics, and data management. Together we build a modern and reliable data foundation that supports decision making across the business. This role is in the Data Management & New Initiatives team.
About the role:
You'll design and implement data quality frameworks across our Databricks and dbt environment. This includes automated testing, validation rules, anomaly detection, and monitoring of data quality metrics. You'll partner with data owners, analysts, and engineers to define what "good data" means for each domain. You'll investigate quality issues, find root causes, and drive lasting fixes rather than quick patches. You'll build the checks, tests and monitoring that catch problems early, and work with teams across the bank to fix them at the source.
This is a great opportunity to enjoy a high degree of self-responsibility and to expand your expertise into areas of your interest. You'll collaborate broadly and see the tangible results of your work.
We offer a hybrid working model with two common in-office days. While we value flexibility, we also prioritize collaboration, team building, and enjoying fikas and after work gatherings together! You'll also have the chance to work with cutting-edge data technologies and be part of shaping the future of NOBA's data landscape.
Main tasks:
Design and implement data quality checks and automated tests in Databricks and dbt pipelines.
Define, measure, and report on data quality metrics together with data owners and stakeholders.
Build monitoring and alerting to detect anomalies, schema changes, and freshness or completeness issues.
Investigate data quality incidents, perform root cause analysis, and implement lasting fixes.
Contribute to data governance work such as data contracts, lineage, and documentation.
Use version control and CI/CD to integrate quality checks into the development workflow.
Ensure security and compliance throughout the data lifecycle, in line with banking requirements.
Document processes and contribute to best practices.
We are looking for you who:
Takes pride in data that people can trust. You have a sharp eye for detail and a curious, investigative mindset, and you enjoy digging into why something looks wrong and making sure it doesn't happen again.
For this role, you'll need:
Experience as a Data Engineer or in a similar role, with a clear focus on data quality, testing, or validation.
Fluency in English, along with fluency in either Swedish or Norwegian.
An academic degree in a relevant field such as data engineering, computer science or a related discipline.
Practical experience with both Databricks and dbt in a production environment.
Experience with data observability or data governance tools (e.g., data catalogs, lineage).
Strong SQL skills and a solid understanding of ETL/ELT processes and data logic.
Experience implementing data tests and validation (e.g., dbt tests, Great Expectations, or similar).
Hands-on experience with Microsoft Azure's data stack.
Solid understanding of version control and CI/CD practices.
It's positive if you have:
Python experience.
Previous experience in data-focused roles within the financial sector.
Familiarity with Agile and Scrum methodologies.
To thrive and perform well in the role, we believe that you need to be:
You're proactive and collaborative, you communicate clearly with both technical and business colleagues, and you approach challenges with a "can-do" mindset. You value a high degree of self-responsibility and are motivated to grow in the areas that interest you.
In addition to this, it is important to us that you recognize yourself in our values: we are wholehearted, we collaborate and we take the lead.
Our offer to you
Freedom with responsibility: Flexible working arrangements to suit your work style and a high degree of self-responsibility.
Career development: Opportunities to grow and develop in your role, and…