Remote Data Engineer: Databricks, Cloud Pipelines & Growth

Conclusion Lifecycle; Portugal; Presencial; EUR 55 000 - 90 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Remote work possible

Career development

Continuous training & certifications

Internal mobility program

Resumo da oferta

Conclusion Lifecycle is seeking a Data Engineer to join a nearshore remote team serving European clients. You will design and implement scalable data pipelines on cloud platforms, focusing on Databricks and Snowflake, and translate business needs into robust data models.

You will collaborate with data scientists and analytics teams to deliver high-quality analytics-ready datasets, ensuring data quality through monitoring and CI/CD practices.

Qualificações

• Design, develop, and maintain scalable data pipelines in cloud environments.
• Proficient in SQL and Python for data tasks.
• Knowledge of data modelling: 3NF, dimensional, Data Vault.
• Experience with Databricks and Snowflake platforms.
• Familiar with cloud ecosystems: Azure, AWS, GCP.
• Understand DevOps/CI‑CD in data workflows.
• Strong communication with business and tech teams.

Responsabilidades

• Design, develop, and maintain scalable data pipelines on cloud platforms (Azure/AWS/GCP).
• Build and optimize batch and streaming processing for large datasets.
• Translate requirements into data models (3NF, dimensional, Data Vault).
• Deliver analytics-ready datasets using Databricks and Snowflake.
• Ensure data quality and reliability with monitoring and validation.

Conhecimentos

SQL

Python

Data modeling

DevOps

CI/CD

Data visualization

Communication

Ferramentas

Databricks

Snowflake

Azure

AWS

GCP

Descrição da oferta de emprego

Conclusion Lifecycle is seeking a Data Engineer to join a nearshore remote team serving European clients. You will design and implement scalable data pipelines on cloud platforms, focusing on Databricks and Snowflake, and translate business needs into robust data models.

You will collaborate with data scientists and analytics teams to deliver high-quality analytics-ready datasets, ensuring data quality through monitoring and CI/CD practices.