Engenheiro(a) de Dados Pleno (4)

Accountabilities:
As a Data Engineer, you will be responsible for designing, developing, and maintaining scalable data solutions that enable organisations to leverage information effectively. You will collaborate with multidisciplinary teams, contribute to technical discussions, and help deliver secure, high-performance data platforms. Develop data pipelines for data ingestion, transformation, processing, and delivery at scale. Work with modern data architectures, including Lakehouse, Data Mesh, and Medallion approaches. Ensure data quality, traceability, versioning, validation, and secure reprocessing processes. Support the implementation of DataOps practices, monitoring strategies, and data observability solutions. Build solutions focused on performance, governance, reliability, and security. Collaborate with architects, data scientists, and analysts to design effective data solutions. Participate in technical discussions with clients, recommending best practices and efficient approaches. Create and maintain clear technical documentation for data processes and architectures. Requirements: The ideal candidate is a data engineering professional with experience building scalable data platforms and working with cloud-based environments. You should have strong technical skills, a problem-solving mindset, and the ability to collaborate with teams and stakeholders in complex projects. Professional experience with ETL/ELT processes, including data collection, transformation, and loading workflows. Experience working with cloud and on-premise data architectures, particularly Azure and/or Google Cloud environments. Knowledge of data pipeline monitoring, optimisation, and troubleshooting. Experience implementing data quality, validation, and integrity strategies. Experience building and managing Data Lakes and Data Warehouses. Strong programming skills in Python and advanced SQL. Experience with relational and non-relational databases such as SQL Server, PostgreSQL, MongoDB, or similar technologies. Knowledge of data platforms and tools such as Azure Data Factory, Synapse, Databricks, Fabric, or equivalent solutions. Experience with Git-based version control workflows. Understanding of DataOps concepts, data governance, and best practices. Ability to create structured and clear technical documentation. Experience with dbt, Airflow, Dagster, Databricks, Snowflake, BigQuery, Microsoft certifications, AI projects, MLOps, or advanced analytics is considered a plus. Benefits: Remote or hybrid work model with flexibility. Opportunity to work on challenging projects with large organisations across different industries. Collaboration with an experienced and technically strong team. Exposure to Microsoft technologies and emerging data and AI solutions. Freedom to propose solutions and contribute to technical decisions. Continuous learning opportunities through certifications, training programs, and technical communities. Professional growth opportunities within a dynamic technology environment.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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