Freelance Oracle Data Warehouse Consultant
Nordea Cyber Security needed a stable and well-defined data foundation for analytics, where both data modeling and operational reliability had to align in practice, because security data is rarely "clean," and the requirements for traceability and robustness are higher than in ordinary reporting. I worked as a freelance Oracle Data Warehouse Consultant in a hybrid model (on-site alternately in Copenhagen and Gdynia), primarily for Cyber Security Analytics, where the focus was to design and maintain data models, stored procedures, and pipelines on Oracle, and to ensure the solution could run stably and predictably in operation, even as data and requirements evolved.
Nordea Cyber Security Analytics needed a stable data foundation that could be used consistently across analysis needs, while simultaneously being operated predictably in an environment with high demands for traceability and robustness. The goal was to ensure that models and transformation logic aligned so that data could be reused, validated, and further developed over time, without becoming a fragile setup that only worked when everyone performed every action in the correct order.
Data modeling took place in an environment where the structure had grown organically over time, resulting in a mix of different approaches and some inconsistency across data models and pipelines. Resources were not allocated for a full cleanup or a total redesign of the model, so the focus became a pragmatic approach within the existing framework—creating more consistency where it provided the most effect and ensuring that new deliveries fit in without unnecessarily increasing complexity.
Transformation logic and data flows were realized in practice through a combination of stored procedures in Oracle and orchestration via DataStage. The focus was on keeping transformation logic close to the data where it made sense, while ensuring DataStage pipelines could be executed stably, re-run in a controlled manner, and understood by someone other than the person who last modified them.
In a security analytics environment, operational reliability and traceability are not just extras—they are the prerequisite for data being usable at all, as one otherwise ends up with results that cannot be explained or reproduced. Therefore, a significant part of the work was ensuring more predictable operations, better troubleshooting, and more clear traceability in transformation logic and data flows, so that changes could be carried out without creating hidden side effects.
The work took place in an international setup with on-site periods alternating between Copenhagen and Gdynia. The purpose was to have a close contact interface with users and stakeholders in Copenhagen, while also collaborating directly with the team in Gdynia, so that clarifications and deliveries were not slowed down by distance, misunderstandings, or long handover chains.
Nordea is one of the largest financial groups in the Nordics and provides banking and financial services to both private customers and businesses. As a major bank, Nordea operates across borders, regulatory requirements, and business areas, meaning that data, processes, and governance take up a lot of space in daily life, including the technical environments where data solutions are developed and operated.
Such an organization differs from smaller companies in that complexity is often a basic condition, both in the system landscape, integration patterns, and organization, where many teams and stakeholders contribute to the overall delivery. This typically places high demands on stability, traceability, and auditability, because errors and inconsistencies quickly have consequences across several areas simultaneously.
In this context, Cyber Security is a central discipline because financial institutions are an obvious target for cyberattacks, both due to financial value and because attacks can exploit dependencies between systems, data flows, and access management. Consequently, work with security analytics, operational reliability, and traceability typically carries more weight than in many other domains, as data and results must be explainable, reproducible, and used as a basis for action.