Data Engineer (Azure) - Remote, Latin America

Bluelight Consulting • Stratton, VT

2d • Remote

About The Position

As an ETL Data Engineer, you will play a critical role in our client’s expanding data engineering team, designing, developing, and maintaining data integration processes primarily using Python (PySpark) and Azure Synapse Analytics to ensure the accuracy and availability of analytical data. Working closely with data scientists, analysts, and other stakeholders to deliver high-quality data for insights and decision-making, this position is ideal for a passionate software development professional who thrives in a fast-paced, dynamic environment where everyone's opinions and efforts are valued. By joining our client’s growing software consultancy, you will have the opportunity to contribute to challenging, market-standing projects within a collaborative community that deeply values hard work, continuous learning, personal growth, and professional development.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, or a related field; or equivalent work experience.
• 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.

Nice To Haves

• Certifications related to data engineering or data science (e.g. Azure Data Engineer).
• Familiarity with big data technologies, machine learning, and data analysis.
• Experience with data visualization tools (e.g. Power BI, Tableau).
• Experience with Agile Methodologies.

Responsibilities

• Develop and maintain ETL data engineering processes using Python (PySpark) within Azure Synapse Analytics Notebooks, and/or Azure Synapse Analytics Pipelines, to ensure efficient data extractions, transformation, and loading.
• Apply expertise in data warehousing, understanding star schemas, facts, and dimensions, to design and build effective data storage structures in a Massively Parallel Processing (MPP) SWL Pool.
• Extract data from various sources, including REST APIs, SWL database tables, and CSV files.
• Utilize deep knowledge of Azure Synapse Analytics to design and optimize data notebooks/pipelines for scalability and performance.
• Contribute to the implementation and understanding of other Data Fabric concepts, such as data lakes, lakehouses, delta lakes, and data cataloging, to enhance data management capabilities.
• Collaborate with data architects to create data models and schemas that align with business requirements.
• Implement data quality checks and validation processes to maintain data accuracy and consistency.
• Identify and resolve performance bottlenecks and optimize ETL data notebooks/pipelines to meet SLAs.
• Monitor ETL jobs, diagnose issues, and implement solutions to ensure data pipeline reliability.
• Maintain comprehensive documentation of ETL data engineering processes, data flows, and data transformations.
• Work closely with cross-functional teams to understand data requirements and provide support for data-related initiatives.
• Ensure data security and compliance with data governance and privacy standards.

Benefits

• Opportunity to work on a variety of projects for clients.
• Accelerated career growth.
• Collaboration with modern technologies.
• Work alongside some of the best professionals in the industry.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree