Senior Data Engineer - Feature Engineering for AI Models
About this role
Job ID: 3147
We are looking for a Senior Data Engineer to join our Feature Engineering team. You will play a key role in transforming big and complex data into meaningful features that enables machine learning model to predict patterns to fight against financial crime.
About our team
Meet the AI & Machine Learning team. Our role is to improve how we detect and investigate financial crime with the help of AI and machine learning, to keep our customers, Nordea and society safe. Our team is about 25 people working out of Stockholm, Helsinki and Copenhagen as a part of the group financial crime prevention (GFCP) unit that provides financial crime prevention services to all of the bank.
Collaboration. Ownership. Passion. Courage. These are the values that guide us in how we work and how we make decisions – and that we imagine you share with us.
What you’ll be doing:
Design and build data pipelines to enable machine learning based threat detection capabilities
Utilize AWS technologies to create and maintain efficient data transformation workflows
Work closely with data scientists, platform and data specialists
Ensure the solutions meet data quality and governance standards
Document and communicate the results of your efforts
Who are you
We believe that you're ambitious and enjoy bringing ideas, exploring their potential, and presenting the results. You're passionate about designing and building data pipelines to produce features for machine learning models to identify complex financial crimes, ensuring both the bank’s and society's safety.
Your experience and background:
At least 5 years of experience creating and optimizing data pipelines and processing large data sets
Experience with AWS and services: Glue, S3, Athena, Sagemaker AI, Step Functions and Event Bridge
Proficiency in Python, PySpark and SQL
A degree in data engineering, computer science or related field
Self-driven team player with curious mindset
Ability to communicate clearly on complex topics
Fluency in