Data Scientist / Data Engineer

Data Scientist / Data Engineer

Location: Eindhoven, Netherlands

Project Duration: Approximately 1 year, with possible extension

Work Model: Hybrid – 2 days onsite / 3 days remote

Working Hours - 36-40 Hrs/Week

Experience: Minimum 4 years relevant experience

Language: Professional English

Role Overview

The Data Scientist / Data Engineer will develop and maintain a transaction monitoring data model, process large volumes of data, and build scalable, production-ready data solutions.

This role is suitable if you have at least 4 years of relevant work experience as a Data Scientist or Data Engineer and have previously worked with PySpark.

Must-Have Requirements

Minimum 4 years of relevant professional experience.

Experience as a Data Scientist or Data Engineer.

Strong hands-on experience with PySpark.

Strong Python experience.

Experience with data transformations.

Experience with scalable and large-volume data processing.

Ability to develop production-ready code.

Professional proficiency in English.

Strong communication skills.

Strong problem-solving ability.

Proactive working approach.

Ability to work effectively with developers, data producers and other stakeholders.

Ability to critically reflect on own work and development.

Master’s degree in Computer Science, Artificial Intelligence, Mathematics, Information Studies, or a comparable STEM discipline.

Nice-to-Have Requirements

Databricks.

Airflow.

Microsoft Azure.

Medallion Architecture.

Experience working in an IT environment.

Knowledge or experience in transaction monitoring.

Experience working with large-scale international data models.

Key Responsibilities

Develop and maintain the transaction monitoring data model.

Connect and process data from multiple sources.

Process large data volumes in a scalable manner.

Write, optimise and maintain production-ready code.

Perform data transformations using Python and PySpark.

Help build and maintain a monorepository.

Support onboarding of new regions into the data model.

Coordinate with data producers regarding data sources and data quality.

Work with developers who build transaction monitoring logic.

Help ensure the technical soundness and reliability of the data model.

Technical Environment

Python | PySpark | Databricks | Airflow | Azure | Data Transformation | Scalable Data Processing | Transaction Monitoring

Tgineering #DataScience #BigData #DataTransformation #TransactionMonitoring #MedallionArchitecture

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