echoloc
Get started
echoloc/ Companies/ Danske Bank

Danske Bank Tech Stack

Nordic banking group modernizing legacy systems with cloud and AI

Financial Services Copenhagen, Capital Region of Denmark 10,001+ employees Founded 1871 Public Company
danskebank.com LinkedIn

Danske Bank is replacing decades-old mainframe languages (COBOL, PL/I, DB2, SAS) with Python, Java, and cloud infrastructure (AWS, Kubernetes, Databricks). The hiring surge—122 roles posted in 30 days across engineering, data, and security—reflects an aggressive modernization agenda: migrating ETL to cloud, building ML models (TensorFlow, PyTorch, Langchain), deploying Copilot Studio chatbots, and overhauling validation and fraud detection. This is a large financial institution in active technical transformation, not incremental upgrades.

Tech Stack 200 technologies

Core StackPython Java RabbitMQ Kafka AWS Docker Kubernetes OpenShift JUnit Azure DevOps Collibra Tableau SWIFT Databricks MLflow Terraform PySpark Databricks Unity Catalog TensorFlow PyTorch Langchain Hugging Face VBA Alteryx Camunda Camunda Modeler Spring Boot IBM MQ ISO 20022 AWS Step Functions+170 more
AdoptingDatabricks dbt Delta Lake AWS Pinecone Copilot Studio pgvector Chroma+4 more
ReplacingSAS COBOL PL/I DB2

What Danske Bank Is Building

◆Challenges

  • Modernising core banking systems
  • Preventing fraud
  • Migrating legacy etl to cloud
  • Optimizing processes
  • Compliance with aml regulations
  • Migrating to databricks and aws
  • Improving validation efficiency
  • Complex decision processes
  • Standardising validation processes
  • Optimized delivery

▲Active Projects

  • Third-party risk rating model
  • Standardised validation processes
  • Business continuity exercises
  • Implementing chatbot based on microsoft copilot studio
  • Cloud migration for hr data pipelines
  • Ai city platform development
  • Crm system implementation using microsoft dynamics sales 365
  • Technical tooling and automation for validation
  • Strategic initiative
  • Superfly analytics platform expansion

Hiring Activity

Accelerating140 roles · 120 in 30d

Department

Engineering
37
Finance
22
Ops
18
Data
15
Support
8
Operations
7
Security
6
HR
5

Seniority

Senior
59
Mid
52
Lead
10
Junior
7
Intern
3
Manager
2
Principal
2
C-Level
1

Notable leadership hires: Chapter Lead, Red Team Lead, Risk Analyst Lead

Company intelligence

Find more companies like Danske Bank by tech stack, pain points and active projects

Get started free

About Danske Bank

Danske Bank is a public financial services firm headquartered in Copenhagen with over 20,000 employees across the Nordic region. The bank serves individuals, businesses, and institutional clients through retail banking, corporate banking, private banking, asset management, insurance, and mortgage finance. Core operations span personal deposits and lending, business credit, wealth advisory, and real-estate brokerage. The organization is transitioning from legacy monolithic systems toward cloud-native architecture, modernizing risk management, and integrating AI-driven advisory and customer-facing tools.

HeadquartersCopenhagen, Capital Region of Denmark
Company Size10,001+ employees
Founded1871
Hiring MarketsPoland, Denmark, Lithuania, Sweden

Frequently Asked Questions

What is Danske Bank's tech stack?

Core languages: Python, Java, VBA. Cloud: AWS, Azure DevOps, Kubernetes, Docker, OpenShift. Data: Kafka, RabbitMQ, Databricks, Snowflake connectors, Delta Lake, PySpark. ML: TensorFlow, PyTorch, Langchain, Hugging Face, MLflow. Legacy: still running COBOL, PL/I, DB2, SAS (being replaced).

What is Danske Bank working on?

Cloud migration (HR data pipelines, legacy ETL, Databricks + AWS), AI/chatbots (Copilot Studio implementation), risk models (third-party rating, fraud prevention, AML compliance), CRM (Dynamics 365), and validation automation. Active modernization of core banking systems.

How this profile is built

Danske Bank's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.