Data Engineer - DataBricks Lisboa

Data Engineer - DataBricks
Lisboa
Lisboa, Lisboa, Portugal

Job Title: Data Engineer - Data
Bricks

Location: Lisboa

Work Regime: Hybrid

Step into a dynamic ecosystem where the future of business is created and lived every day. Be part of it!

At LUZA Group, passion, perseverance, and the drive to excel define our path to success.

Founded in 2006, we are a Portuguese multinational with over 1, 200 talented professionals and a turnover surpassing €30M. With a strong presence in key markets—Portugal, Spain, Morocco, Brazil, Mexico, the United States, and China—we deliver innovative solutions in engineering, IT, design, consulting, Industry 4. 0, training, and recruitment. Our people and their talent power everything we do.

This is a moment of growth and opportunity. The future belongs to visionary minds. Will you join us?

About the Position

As a Data Engineer, you will be responsible for understanding business and technological challenges, develop data pipelines tackling those challenges, and ensuring their smooth deployment.

You will as well be responsible of the applications of standard industry and
-
- company good practices, and the application and evolution of our various patterns.

Responsibilities

Projects understanding and Communication

• Understand problems from a user perspective and communicate to clearly understand the issue.
• Ensure the architecture provided by the Data Architect is clearly understood by yourself.
• Communicate with the Data Architect and your peers on the technical solution youre developing and communicate with the Project Manager in charge of the project youre working on.

Development

• Write and communicate on new or updated interface contracts.
• Strong understanding of data warehousing concepts, data lakes, ETL/ELT processes, and data modeling.
• Develop data pipelines based on the defined architecture.
• Ensure the regular good practices are applied.
• Deploy requested infrastructure, particularly using Terraform.
• Make peer reviews and ask to your peers to review your code when merging a new version of the codebase.

Testing

• Define tests with your project manager, based on the functional and technical requirements of the pipeline youre developing.
• Perform those tests and communicate regularly on the results.
• Regularly summarize the results of your tests in a dedicated document.

Deployments

• Present to the Data Architect in charge of the architecture, and the Lead Data
  Ops, the development that was performed through our Deployment Reviews.
• Track and communicate on any potential errors in the entire period of active monitoring following a deployment.
• Ensure diligent application of deployment process, logging, and monitoring strategy.

Requirements

• Proficiency with Py
  Spark and Spark SQL for data processing.
• Experience with Databricks using Unit Catalog.
• Knowledge of Delta Live Tables (DLT) for automated ETL and workflow orchestration in Databricks.
• Familiarity with Azure Data Lake Storage.
• Experience with orchestration tools (e. g. , Apache Airflow or similar) for building and scheduling ETL/ELT pipelines.
• Knowledge of data partitioning and data lifecycle management on cloudbased storage.
• Familiarity with implementing data security and data privacy practices in a cloud environment.
• Terraform: At least one year of experience with Terraform and know good practices of Git
  Ops.
• Additional Knowledge and Experience that are a Plus:
  • Databricks Asset Bundles
  • Kubernetes
  • Apache Kafka
  • Vault

Personal Traits

• Ability to adapt to different contexts, teams and stakeholders
• Proactive Ownership for the projects delivered by your team
• A continuous eye for improvement on existing processes
• Clear communication and collaboration skills
• Excellent analytical /
  - solving ability
• Demonstrated ability to manage your stress in an operational environment
• Demonstrated ability to understand quickly business and technical requirements of a data pipeline that needs to be developed, and challenge potential misconceptions
• The candidate should have worked in a Data Platform that was in an industrial scale

#Visionary
Future - Build the future, join our living ecosystem!