Data Engineer managing data pipelines and analytics for strategic data projects in corporate environments. Working with data governance, integration, and BI initiatives.
Responsibilities
Develop and maintain data ingestion, transformation and loading pipelines (ETL/ELT) from multiple sources (APIs, relational databases, files, legacy systems and cloud)
Work on data modeling (Data Lake, Data Warehouse and Data Marts), ensuring performance, scalability and governance
Process large volumes of data using Spark (PySpark or Scala) and Big Data frameworks
Develop scripts and automations in Python for data extraction, integration and processing
Create and optimize advanced SQL queries (procedures, views, functions, jobs), with a focus on performance
Work in cloud environments (GCP, Oracle Cloud, Azure or similar), participating in data migration and modernization projects
Integrate data across different platforms and systems (e.g., CRM, marketing, surveys, corporate systems)
Support the development of dashboards and reports in Power BI, ensuring reliable data for analysis and decision-making
Collaborate with business, analytics and technology teams to understand requirements and translate them into data solutions
Document processes, models and data pipelines, following engineering and governance best practices
Requirements
Proven experience as a Data Engineer or in similar roles
Advanced SQL proficiency, including query optimization and database objects
Strong experience in ETL/ELT using tools such as PowerCenter, Pentaho, Databricks, SSIS or equivalents
Practical knowledge of Python for data engineering and automation
Experience with distributed processing (Spark – PySpark or Scala)
Experience in cloud environments (Google Cloud, Oracle Cloud, Azure or similar)
Experience with Data Lakes, Data Warehouses and data modeling
Knowledge of data integration via APIs, files and secure transfer (SFTP)
Ability to communicate with technical and business stakeholders
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