Azure Data Engineer - Remote, Latin America
Remote · David, Chiriquí Province, Panama
Job Summary
Design, develop, and maintain ETL data pipelines using Python (PySpark) within Azure Synapse Analytics to ensure accurate, scalable data extraction, transformation, and loading; build data warehouses with star schemas and dimensions, apply data quality checks, and optimize performance while collaborating with data scientists, analysts, and cross-functional teams; leverage Azure Synapse Notebooks and Pipelines, data lakes/lakehouses, and data cataloging, and ensure security and governance-compliant data flows; document data engineering processes and support analytics with visualization tools (Power BI/Tableau) in a fast-paced consultancy environment.
Required Qualifications
- Bachelor’s degree in Computer Science, Information Technology, or a related field; or equivalent work experience, with certifications related to data engineering or data science (e.g. Azure Data Engineer) being a plus.
- Proven experience in ETL data engineering with significant expertise in using Python (PySpark) to perform data extraction, transformation, and loading from REST APIs, SQL database tables, and CSV files.
- Proficiency in using Azure Synapse Analytics resources including Notebooks, Pipelines, Linked Services, and Azure Key Vault.
- Demonstrated ability to write complex SQL queries, optimize query performance, and work with both SparkSQL and MS SQL to effectively extract, transform, and load data.
- Knowledge of data integration best practices and tools.
- Experience with version control systems, such as Git (Azure DevOps).
- Strong problem-solving and analytical skills, with a keen attention to detail.
- Excellent communication skills, both verbal and written, with the ability to work collaboratively in a team environment with shifting priorities.
- Familiarity with big data technologies, machine learning, and data analysis preferred.
- Experience with data visualization tools (e.g. Power BI, Tableau) and Agile Methodologies a plus.
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