Data Engineer

Job Title: Data Engineer - Azure & Machine Learning (Mid-Level)

Location: Portugal

Work Model: Full-time & Remote

About Us

MOZAYDO was built by people who believe work should feel human - even when powered by technology.

We’re a remote-first company that connects talent, technology, and purpose to help companies grow sustainably.

We also believe recruitment should be more than matching keywords to job descriptions. It's about understanding people, recognising potential, and creating the right conditions for long-term success. Here, ownership matters more than control, and progress matters more than perfection.

People powered, tech enabled.

About the Role

We're looking for someone who can bring their expertise, ambition, and unique perspective to one of the project teams we support.

At MOZAYDO, we believe great careers are built where talent, purpose, and opportunity meet. While we're currently hiring for a specific project environment, our focus goes beyond matching skills to requirements. We want to understand where you can create the most impact and continue growing.

In this role, you will build data solutions on Microsoft Azure and contribute to traditional Machine Learning projects, including classification, regression, clustering and/or time-series forecasting. Working with business stakeholders, Data Scientists and technical teams, you will help prepare data, train and evaluate models, and integrate them into production pipelines.

Responsibilities

• Gather and analyse requirements with business and technical teams.
• Develop and maintain ETL/ELT pipelines using Azure Databricks, PySpark and, where applicable, Azure Data Factory.
• Implement data ingestion, transformation and modelling within Data Lakehouse architectures.
• Prepare datasets for Machine Learning, including data cleaning, missing-value handling and feature engineering.
• Contribute to model development, training and evaluation, selecting metrics appropriate to the business problem.
• Implement batch inference and scoring pipelines, integrating model outputs into data processes.
• Monitor data quality and model performance in production.
• Apply good practices in version control, documentation, testing and software development.

Requirements

• Mid-Level: 2-3 years of experience in Data Engineering, including participation in traditional Machine Learning projects.
• Strong practical experience with Azure Databricks.
• Good knowledge of Python, SQL and PySpark.
• Experience with Azure Data Lake Storage, Parquet and Delta Lake.
• Experience across the data solution lifecycle: ingestion, transformation, modelling and delivery.
• Hands-on experience with Machine Learning libraries, data preparation, feature engineering, model training, validation and evaluation.
• Understanding of overfitting, data leakage, train/test separation and cross-validation.
• Experience integrating models into data processing and batch scoring pipelines.
• Experience with Git and good software engineering practices.
• Ability to develop scalable solutions with attention to performance and execution costs.
• Analytical thinking, autonomy, attention to detail and the ability to translate business needs into technical solutions.
• Strong communication and collaboration skills.
• Native Portuguese; intermediate English.

Nice to have:

• Azure Machine Learning, including training jobs/pipelines, model registration and inference endpoints.
• Azure Data Factory.
• Model monitoring, data drift detection and retraining processes.
• Model deployment through APIs or online endpoints.
• Unity Catalog, data governance and access control.
• Scrum, Jira and Confluence.

Hiring Process

Our hiring process is designed to help both sides make an informed decision and ensure the best possible match between your experience, aspirations, and the project environment. Please note that the process may vary depending on the complexity and specific requirements of each opportunity.

• Initial Conversation with Us: a first discussion focused on understanding your background, strengths, motivations, and the type of environment where you can thrive and create the greatest impact.
• Project Team Qualification: a conversation with the project team to explore the role, responsibilities, expectations, and mutual alignment.
• Role-Specific Evaluation (when applicable): depending on the position, this stage may include technical assessments, practical exercises, certification validation, personality assessments, security clearance requirements, or other evaluation methods relevant to the role.
• Final Alignment Meeting: a final conversation to ensure expectations are aligned on both sides and that, if we move forward together, we start with clarity, confidence, and shared objectives.