Join our team as a Data Engineer and play a pivotal role in shaping our data landscape. You will be instrumental in designing, developing, and maintaining robust data solutions that drive data generation, collection, and processing.

Your primary focus will be on building efficient data pipelines, ensuring the highest standards of data quality, and implementing sophisticated ETL processes for seamless data migration and deployment across various systems. This role offers the opportunity to collaborate closely with cross-functional teams, understand intricate data requirements, and contribute significantly to our organization's data strategy, ensuring our solutions are not only effective but also scalable and aligned with core business objectives. Continuous monitoring and optimization of existing data processes will be key to enhancing performance and reliability, fostering data-driven decision-making to achieve organizational success.

As a Data Engineer, you are expected to operate with a high degree of autonomy and aspire to become a subject matter expert. Your active participation in team discussions and contributions to problem-solving will be highly valued.

You will collaborate with stakeholders to meticulously gather and analyze data requirements. A key part of your role involves designing and implementing data models that effectively support evolving business needs. You will leverage your expertise in programming languages such as Python and SQL, alongside big data technologies like Spark, to create powerful data solutions. Experience with cloud platforms, particularly Microsoft Azure, is essential, with a focus on Azure Databricks and Azure Data Factory.

Proficiency in programming languages like Python and SQL, coupled with experience in big data technologies such as Spark, is a core requirement. You should have hands-on experience with cloud platforms, primarily Microsoft Azure, and specifically with Azure Databricks and Azure Data Factory. Familiarity with CI/CD processes and tools like Azure DevOps, Jenkins, and Git is necessary for ensuring efficient deployment of data solutions.

Understanding of APIs for data integration and the ability to interpret high-level design documents and translate them into actionable development tasks are also important. A good understanding of the Microsoft data stack, including Azure Data Factory, Azure Synapse, Databricks, Azure DevOps, and Fabric/PowerBI, will be beneficial. Experience with machine learning and AI technologies, ETL pipeline design, Azure DevOps, and logging/monitoring using Azure/Databricks services, along with Apache Kafka, are considered advantageous.