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Salary
$37k – $88k per year (Estimated)
Location
Remote (Brazil)
Seniority
Middle
Employment
Full-Time
Overview
Company
Impact
Profile match
Jobgether is a Belgian recruitment platform built entirely around remote and flexible work, aggregating openings from thousands of employers that allow work from outside an office. Its matching engine ranks roles against a candidate's skills, seniority and stated preferences on location and flexibility, rather than leaving people to filter a keyword search, and it verifies how genuinely remote each posting is. The company also runs an AI screening layer that shortlists applicants for employers, and publishes research and guidance on distributed work practices alongside the job marketplace itself.

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados Pleno based in Brazil.

This is an opportunity for a mid-level Data Engineer to contribute to the development, maintenance, and continuous evolution of modern data solutions. You will help ensure that critical business information remains available, reliable, secure, and high quality across the organization. The role combines hands-on work with data pipelines, ETL/ELT processes, integrations, databases, and cloud environments. You will collaborate closely with Engineering, Analytics, Data Science, BI, and business teams to deliver data solutions that support informed decision-making. The position offers exposure to large-scale data processing and modern architectures such as Data Lake, Data Warehouse, and Lakehouse. It is well suited to a data professional who enjoys solving technical challenges and building dependable data foundations for business growth

Accountabilities:

    • Develop, maintain, and continuously improve data pipelines and ETL/ELT processes, ensuring reliable ingestion, transformation, and delivery of data.
    • Build and maintain integrations between data sources and platforms, supporting the availability and consistency of information used by business and analytical teams.
    • Work with relational databases and large datasets to process, transform, organize, and optimize data according to business and technical requirements.
    • Contribute to data architecture initiatives involving Data Lakes, Data Warehouses, and Lakehouse environments.
    • Monitor and troubleshoot data pipelines and processes, identifying issues and implementing solutions to maintain data quality, availability, security, and reliability.
    • Collaborate with Engineering, Analytics, Data Science, BI, and business stakeholders to understand requirements and translate them into effective data solutions.
    • Apply development and version-control best practices using Git-based tools and contribute to reliable, maintainable data engineering workflows.
    • Support the implementation and evolution of data solutions in cloud environments, adapting to the organization’s infrastructure and technology requirements.
    • Requirements

      • Professional experience working as a Data Engineer or in a comparable data engineering role.
      • Advanced knowledge of SQL, with the ability to develop complex queries and perform data extraction, transformation, analysis, and optimization.
      • Hands-on experience developing ETL/ELT processes and data pipelines.
      • Experience working with relational databases such as SQL Server, PostgreSQL, Oracle, or MySQL.
      • Experience processing and manipulating large volumes of data in production or enterprise environments.
      • Knowledge of Git and collaborative version-control platforms such as GitHub or GitLab.
      • Understanding of Data Lake, Data Warehouse, and Lakehouse concepts and architectures.
      • Professional experience with cloud environments, preferably AWS, Azure, or Google Cloud Platform (GCP).
      • Knowledge of NoSQL databases is considered an advantage.
      • Strong analytical and problem-solving skills, with the ability to investigate data issues and develop practical technical solutions.
      • Good communication and collaboration skills, with the ability to work effectively across technical, analytical, and business teams.
      • Benefits

        • Opportunity to work on the development and evolution of business-critical data solutions.
        • Exposure to modern data engineering practices, including ETL/ELT, cloud platforms, Data Lake, Data Warehouse, and Lakehouse architectures.
        • Collaboration with multidisciplinary teams across Engineering, Analytics, Data Science, BI, and business functions.
        • Opportunity to work with large-scale datasets and cloud-based data environments.
        • Professional growth through hands-on experience with modern data technologies and enterprise data challenges.
        • Additional salary, healthcare, leave, flexibility, and employee benefits may be provided according to the partner company’s applicable policies.
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