MACHINE LEARNING ENGINEER – MID (HYBRID)

MACHINE LEARNING ENGINEER – MID (HYBRID LISBON)

Portuguese company hires for hybrid position

📍 Location: Lisbon, Portugal

⚠️ Only candidates already based in Portugal will be considered

💼 Work Model: Hybrid — 60% remote and 40% on-site

🗣️ Language Requirements: English B2+ — Mandatory

🕓 Seniority: Mid-level (3+ years)

🏭 Sector: Telecommunications

⚠️ Instructions: Please send your CV in English and make sure to include all skills and experience that match the requirements of the opportunity. This will significantly increase your chances of success

About the Opportunity

You will work at the intersection of Data Engineering, Machine Learning, and Data Science, helping create robust data products that support recommendation systems and business decision-making.

This opportunity is particularly suited to a Data or Machine Learning Engineer who already has strong production pipeline experience and wants to develop further in Machine Learning, MLOps, and Data Science.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Python, PySpark, and SQL.
• Orchestrate and monitor workflows using Airflow and Astronomer.
• Implement automated testing, deployment, and CI/CD processes with GitHub.
• Ensure data quality, reliability, observability, and timely delivery according to agreed SLAs.
• Monitor production pipelines and troubleshoot operational incidents.
• Automate recurring operational and maintenance tasks.
• Structure reusable data products within a data mesh environment.
• Work with distributed systems and large-scale datasets.
• Collaborate closely with Data Scientists, Product Owners, technical teams, and business stakeholders.
• Support the progressive integration of Machine Learning and AI models into production.
• Take on additional Data Science responsibilities as the recommendation products evolve.

Mandatory Requirements

• Degree in Computer Science, Engineering, Mathematics, Statistics, or another related quantitative field.
• At least 3 years of experience building and operating production data pipelines.
• Strong programming skills in Python and/or PySpark.
• Strong SQL knowledge for data transformation, analysis, and validation.
• Hands-on experience with Airflow or a similar workflow orchestration tool.
• Experience with Data Engineering and scalable data pipelines.
• Understanding of distributed computing and large-scale data processing.
• Experience with data modelling, testing, and validation.
• Knowledge of data quality and observability practices.
• Experience implementing or working with CI/CD processes.
• Familiarity with Machine Learning workflows.
• Strong motivation to progress towards Machine Learning, MLOps, and Data Science.
• Strong analytical, troubleshooting, and problem-solving skills.
• English proficiency at B2 level or higher.
• Availability to work under a hybrid model with 40% on-site presence.

Five Core Required Skills

• Python and/or PySpark — development of production data pipelines.
• SQL — data transformation, analysis, and validation.
• Airflow — workflow orchestration and monitoring.
• Data Engineering — scalable pipelines, distributed processing, and production data.
• Data Quality and Observability — automated testing, validation, data contracts, monitoring, and SLA compliance.

Nice to Have

• Experience with Google Cloud Platform.
• Knowledge of BigQuery and Dataproc.
• Experience with Astronomer.
• Understanding of data mesh architectures.
• Experience with MLOps practices and tools.
• Knowledge of data contracts.
• Experience with data quality frameworks.
• Previous exposure to recommendation systems.
• Experience deploying Machine Learning or AI models into production.
• Knowledge of GitHub Actions or similar CI/CD tools.

The Ideal Candidate

The ideal candidate has a strong Data Engineering foundation combined with knowledge of Machine Learning workflows and a clear motivation to progress towards Data Science.

You are comfortable building and operating scalable pipelines, working with distributed data processing, monitoring production workflows, and ensuring that data products are reliable and delivered within agreed SLAs.

You take ownership of your work, pay close attention to data quality, and understand how technical decisions affect business outcomes. You also enjoy collaborating with Data Scientists, Product Owners, and business stakeholders in an agile environment.

Candidate Questions

Before applying, please confirm:

1. Do you have at least three years of experience building and operating data pipelines in production?
2. How many years of professional experience do you have with Python and PySpark?
3. How would you rate your SQL knowledge?
4. Have you used Airflow to orchestrate and monitor production workflows?
5. Do you have experience with Astronomer?
6. Have you worked with distributed computing and large-scale data processing?
7. What experience do you have with data modelling, automated testing, and validation?
8. Have you implemented data quality, observability, or data contract practices?
9. Do you have experience monitoring pipelines and resolving production incidents?
10. Have you worked with CI/CD pipelines and GitHub or GitHub Actions?
11. Do you have practical experience with Machine Learning or MLOps workflows?
12. Are you interested in progressively taking on more Data Science responsibilities?
13. Have you worked with GCP, BigQuery, or Dataproc?
14. Do you have experience with data mesh environments or reusable data products?
15. Is your English level B2 or higher?
16. Are you available for a hybrid model with 40% on-site presence?
17. Are you aligned with a monthly B2B/RV rate between €1,600 and €1,900?
18. What is your availability to start?

CV Keywords

Machine Learning Engineer, Data Engineer, Data Engineering, Machine Learning, MLOps, Data Science, Python, PySpark, SQL, Airflow, Astronomer, GitHub, GitHub Actions, CI/CD, Data Pipelines, Production Pipelines, ETL, ELT, Workflow Orchestration, Distributed Computing, Large-Scale Data Processing, Data Modelling, Data Transformation, Data Validation, Automated Testing, Data Quality, Data Observability, Data Contracts, SLA Monitoring, Production Monitoring, Incident Troubleshooting, Pipeline Automation, Data Products, Reusable Data Products, Data Mesh, Recommendation Systems, Recommendation Products, AI Models, ML Models, Model Integration, Model Deployment, GCP, Google Cloud Platform, BigQuery, Dataproc, Agile, Telecommunications, SME Customers, Residential Customers, Problem-Solving, Stakeholder Management

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