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A driven Data Engineer who can bridge the gap between complex data management and robust software engineering. You are someone who not only executes tasks but brings new perspectives to the table, helping to evolve our large-scale application landscape that processes millions of records. As a Data Engineer, you are responsible for the end-to-end development and implementation of data pipelines and streaming-based integrations. You will take a lead role in designing how we consume data from diverse sources, applying complex transformations, and ensuring seamless delivery to target systems within the Azure cloud.
Job Responsibility:
Architecting and maintaining a state-of-the-art Data Lake solution
Integrating time-critical fraud detection applications with multiple downstream systems via real-time and streaming interfaces
Mentoring junior team members and catering to the high-level data needs of various business consumers
Develop and perform peer reviews for data solutions deployed in the Azure cloud
Master and implement technologies including ADLS Gen2, Databricks, and Apache Kafka
Act as a core member of a diverse Agile/Scrum DevOps team, taking full ownership of managing and maintaining mission-critical functionalities
Requirements:
Proven experience with PySpark and Databricks in large-scale environments
Strong command of Python and SQL for complex data manipulation
Professional experience with ADLS Gen2 and Azure-based components
Deep understanding of Scrum/Agile principles and DevOps mentalities
Strong exposure to Microsoft Azure (DevOps, Boards, Git, Pipelines) or AWS
Solid understanding of data management, including data retention, quality control, metadata management, and data lineage
Proficiency or a strong drive to implement Workflow Automation via Azure Data Factory, Databricks Workflows, or Apache Airflow
Professional English proficiency
a persuasive communicator who can align stakeholders and lead technical discussions