Data Engineer
At a glance
Mid-level Data Engineering role at Inetum. Ermesinde · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and look after scalable pipelines, including systems that bring in data from different sources
- Improve processing workflows for large datasets and keep data quality, reliability and performance on track
- Work with data science and engineering teams to put machine learning solutions into production and operate them
- Apply software engineering practices and contribute to ongoing improvements
What you bring
- At least three years working in data engineering
- Advanced Python and SQL skills, plus practical experience with PySpark and Polars or a comparable library
- Experience building REST API-based ingestion pipelines and handling large-scale data processing
- Knowledge of software development practices, Git, CI/CD, testing, and production ML pipeline operations
Who this fits
This mid-level role suits a data engineer who can build and optimize large-scale pipelines and support machine learning in production. It is based in Ermesinde with a hybrid arrangement.
From the employer
Inetum is a European leader in digital services. Inetum’s team of 28,000 consultants and specialists strive every day to make a digital impact for businesses, public sector entities and society. Inetum’s solutions aim at contributing to its clients’ performance and innovation as well as the common good.
We are looking for a Mid-Level Data Engineer to design, optimize, and maintain complex data pipelines, ensuring the scalability, reliability, and performance of data solutions. In this role, you will also collaborate with Data Science teams to integrate and operationalize machine learning models in production environments.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines.
- Optimize data processing workflows for large-scale datasets.
- Build and manage data ingestion solutions from multiple sources.
- Ensure data quality, reliability, and performance across platforms.
- Collaborate with Data Science and Engineering teams to deploy and operationalize ML solutions.
- Contribute to software engineering best practices and continuous improvement initiatives.
- Proven experience of 3+ years in Data Engineering.
- Advanced proficiency in Python and SQL.
- Demonstrated ability to design robust, maintainable, and highly scalable solutions for large-scale data processing and performance optimization.
- Hands-on experience with in-memory data processing libraries such as Polars (or equivalent).
- Solid experience with distributed data processing frameworks, particularly PySpark.
- Experience developing data ingestion pipelines leveraging REST APIs.
- Strong knowledge of software development best practices.
- Experience with Git, CI/CD pipelines, and software testing methodologies.
- Experience deploying, monitoring, and maintaining Machine Learning pipelines in production environments.
Nice to have:
- Experience with modern data platforms such as Databricks.
- Experience orchestrating workflows using tools such as Apache Airflow.
- Knowledge of cloud platforms (AWS, Azure, or GCP).
Data Engineering work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Data Engineering
Find me workBy the team behind dataroles.