Data Engineer I developing data services with Azure technology for global risk management insights. Collaborating with teams to optimize data processes and ensure quality standards.
Responsibilities
Design and implement data pipelines to efficiently extract, transform, and load (ETL) data from various sources into a central data lake using Azure Data Factory and Azure Databricks
Transform and process data using Python, PySpark, and SQL to create valuable insights
Collaborate with peers and senior team members to optimize data processes and solve complex challenges
Ensure data quality by performing thorough checks and troubleshooting issues
Apply best practices in data engineering for reliability and performance
Optimize pipelines for maximum efficiency and cost-effectiveness
Document comprehensively and maintain data mapping across multiple systems
Provide hands-on troubleshooting and consulting for data challenges when needed
Perform basic unit testing to maintain data quality standards
Requirements
Bachelor's or master's degree in computer science, information technology, or a related field
Approximately 2-3 years of experience in a data-related role, preferably data engineering
Hands-on expertise with programming languages such as Python, PySpark, and SQL
Working knowledge of data processing frameworks or ETL tools like Azure Data Factory and Azure Databricks
Experience with Azure DevOps or similar CI/CD tools
Experience in agile software engineering methodologies (nice to have)
Strong analytical and problem-solving skills to tackle complex data challenges (nice to have)
Excellent interpersonal and communication skills, both written and verbal (nice to have)
Meticulous attention to detail and commitment to data accuracy (nice to have)
Knowledge of AI tools like CoPilot, ChatGPT, etc. (nice to have)
Eagerness to learn and collaborate with team members (nice to have)
Benefits
hybrid work model where expectation is to be in office at least three days per week
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