**Description:** ---------------- **POSITION MISSION** Responsible for designing, building, and maintaining the data infrastructures that support the company's growth and digital strategy. The primary function is to ensure the availability, reliability, and quality of data, enabling advanced analytics, artificial intelligence, and strategic decision-making. **POSITION RESPONSIBILITIES** * Design, build, and optimize data pipelines for ingestion, transformation, and loading (ETL/ELT). * Manage and maintain data lakes and data warehouses in cloud or hybrid environments. * Integrate structured and unstructured data sources (APIs, databases, logs, etc.). * Ensure data quality, consistency, and governance across the organization. * Collaborate with Data Scientists, Data Analysts, and business teams to enable advanced analytics use cases. * Implement monitoring, versioning, and pipeline orchestration processes. * Optimize query performance and data storage. * Manage data security policies, access controls, and compliance (e.g., GDPR, ISO, HIPAA as applicable). * Document data architecture, processes, and standards. **Requirements:** --------------- **Education:** Professional degree in Systems Engineering, Computer Science, Data Engineering, or related fields. **Experience:** 3+ years of experience as a Data Engineer or in related data roles. Proven experience in building data pipelines and cloud environments. **Desirable Certifications:** ✅ AWS Certified Data Analytics or AWS Data Engineer Associate ✅ Google Professional Data Engineer ✅ Microsoft Certified: Azure Data Engineer Associate **Specific Knowledge:** * Programming languages: Python, SQL (advanced), Java or Scala (desirable). * Frameworks and libraries: PySpark, Pandas, NumPy. * Databases: Relational (PostgreSQL, MySQL, SQL Server) and NoSQL (MongoDB, Cassandra). * ETL/ELT tools: Airflow, dbt, Talend, Informatica, or similar. * Cloud & Big Data: * AWS (Glue, Redshift, S3, EMR), * Azure (Data Factory, Synapse, Data Lake), * GCP (BigQuery, Dataflow, Pub/Sub). * Data Warehousing: Snowflake, BigQuery, Redshift, Synapse. * Orchestration: Apache Airflow, Prefect, or Luigi. * Messaging and streaming: Kafka, Spark Streaming, Pub/Sub. * Data governance and quality: Data Catalog, Collibra, Great Expectations. * Versioning and CI/CD tools: Git, Jenkins, GitHub Actions. **Skills:** Analytical thinking and complex problem-solving. Architectural design capability and systemic vision. Clear communication with technical and business teams. Organization, priority management, and autonomous work. Collaboration in agile and multidisciplinary environments.