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Data Engineer

Published on 26 of May of 2026
Location
Lisboa
Category
Engineers and technicians
Subcategory
Other
Sector
Programming, consulting and other computing activities
working day
Full Time
Work mode
Hybrid (On-site and Remote)
Experience level
Specialist
Department
Engineering

Description

Magic Beans is a specialised consulting firm dedicated to helping organisations adopt and optimise Cloud technologies. With offices in Lisbon, Óbidos, Brussels, Madrid, and Barcelona, we are looking for ambitious professionals who want to grow their careers as Data Engineers while working with top-tier clients and cutting-edge Cloud and Data technologies.

At Magic Beans, great people drive our success. We foster a culture of appreciation, recognition, and continuous growth.

Join our team and grow with us!


Your main responsibilities will include:

  • Customer & Business Understanding, engaging directly with customers to understand their business context, processes, constraints, and pain points, and translating these into clear data, analytics, and platform requirements;
  • Conducting data assessments (maturity, architecture, quality, governance, and gaps), and converting findings into actionable recommendations and scalable implementation roadmaps;
  • Solution Design & Architecture, designing end-to-end data architectures that cover ingestion, storage, transformation, modelling, and serving layers for analytics, BI, and AI use cases, leveraging modern lakehouse patterns;
  • Translating business needs into technical solutions, defining robust, scalable, and future-proof architectures aligned with customer objectives, constraints, and best practices;
  • Designing and implementing scalable data platforms using Databricks and cloud-native data services across AWS, Azure, or GCP;
  • Supporting customers in defining data governance frameworks, including data catalogues, access management, data quality standards, and lineage practices;
  • Data Engineering Delivery, designing, developing, and deploying scalable and reliable ETL/ELT pipelines in cloud environments;
  • Building and optimising data workflows using Databricks, Apache Spark, and cloud-native orchestration services, ensuring performance, cost-efficiency, and scalability;
  • Implementing data models (e.g., star schemas, medallion architecture, and lakehouse patterns) to support analytics, reporting, and machine learning workloads;
  • Ensuring data quality, integrity, lineage, and documentation across all delivered solutions, following best practices in governance and observability;
  • Contributing to platform engineering and DevOps practices, including infrastructure as code (IaC), CI/CD pipelines, and deployment automation.

Minimum requirements

Must-Have Skills

  • At least 3 years of experience in Data Engineering, Data Platform Engineering, or Cloud Engineering roles;
  • Strong experience with major cloud platforms (AWS, Azure, or GCP) and their data ecosystems (e.g., Glue, ADF, BigQuery, Redshift, Synapse, Databricks);
  • Hands-on experience with Databricks, including notebooks, workflows, Delta Lake, and Spark-based processing;
  • Strong knowledge of Apache Spark and distributed data processing concepts;
  • Solid SQL proficiency and experience with relational and analytical data platforms;
  • Strong programming skills in Python (preferred) and PySpark;
  • Experience designing and implementing ETL/ELT pipelines and data transformation workflows;
  • Proven ability to design end-to-end data architectures and translate business requirements into scalable technical solutions;
  • Experience with data modelling techniques (e.g., star schema, medallion architecture, lakehouse patterns);
  • Familiarity with DevOps tools and practices (Terraform, Docker, CI/CD pipelines);
  • Strong analytical, problem-solving, and customer-facing communication skills.


Nice-to-Have Skills

  • Databricks certifications (e.g., Data Engineer Associate/Professional);
  • Experience with data orchestration tools (Airflow, Glue, ADF, MWAA);
  • Knowledge of BI tools (Power BI, Tableau, QuickSight);
  • Experience with ML/AI enablement (feature stores, serving layers, notebook environments);
  • Experience with data quality and observability tools (e.g., Great Expectations, DataFold, Soda);
  • Understanding of data governance frameworks and compliance (ISO 27001, GDPR);
  • Experience working in customer-facing or consulting environments.


Language Skills


  • Fluent in Portuguese and English (spoken and written).