**Semi Senior Data Engineer Role at Procalidad Analytics** Data is the new gold, and we need a skilled data engineer to help us mine it. We're looking for a professional who loves building scalable data solutions, optimizing pipelines, and working with cloud technologies. This role offers the opportunity to join a dynamic team where innovation, automation, and performance are at the heart of everything we do. Required Qualifications - Educational Background: Degree in Systems Engineering, Computer Science, Data Science, Industrial Engineering, or related fields. - Professional Experience: 4+ years in designing, developing, and optimizing data pipelines. **Technical Expertise** - Programming Languages: Strong proficiency in Python (Pandas, NumPy, PySpark) and SQL (Snowflake, PostgreSQL, MySQL, SQL Server). - Data Pipelines & ETL: Hands-on experience in designing, developing, and maintaining scalable ETL processes and data ingestion/transformation workflows. - Databases: Experience with relational and NoSQL databases (MongoDB, Cassandra). - Cloud & Big Data: Experience with AWS (S3, BigQuery, Snowflake) and familiarity with big data frameworks (Hadoop, Spark is a plus). - DevOps & Orchestration: Experience with containerization (Docker, Git) and workflow automation tools like Airflow, Cron Jobs. - Optimization & Performance: Strong knowledge of query optimization, database performance tuning, and best practices in data modeling. - CI/CD Pipelines: Experience in building and maintaining CI/CD pipelines for data solutions. **Key Responsibilities** - Data Pipeline Development: Design, develop, and optimize scalable and efficient data pipelines. - ETL Optimization: Maintain and improve ETL processes for data ingestion, transformation, and storage. - Data Quality & Validation: Implement data quality checks to ensure accuracy and consistency. - Collaboration: Work closely with data scientists, analysts, and engineers to ensure smooth data flow. - Performance Tuning: Optimize SQL queries for scalability and efficiency. - Cloud Data Solutions: Leverage AWS, GCP, or Azure for data storage and processing. - Automation & Monitoring: Automate workflows using Python scripting and monitor data pipelines for reliability and performance. **Soft Skills** - Teamwork – Ability to collaborate effectively in a dynamic environment. - Problem-Solving – Proactive approach to identifying and solving data-related challenges. - Work Under Pressure – Ability to handle deadlines and ensure smooth operations. - Communication – Strong assertive communication skills to interact with cross-functional teams. - Accountability & Responsibility – Ownership of tasks and commitment to objectives.