- Senior Engineer - Data Engineering, with Finance domain having 5.0-30.0 years of experience requires an advanced understanding of data engineering principles and practices, particularly in a cloud-based environment.
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Candidates should possess robust expertise in designing, building, and maintaining scalable data pipelines and systems that facilitate the effective processing and analysis of large datasets.
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Proficiency in leveraging technologies such as Apache Kafka, Apache Beam, and ETL tools for data integration and transformation is essential.
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A deep understanding of data warehousing solutions and database management is critical, alongside the ability to implement efficient data storage strategies that ensure data accessibility and reliability.
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Solid grasp of cloud computing platforms, especially Google Cloud and Microsoft Azure, is necessary for managing data workflows using services like BigQuery and Azure Data Factory.
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Understanding of data modeling, governance, and quality frameworks is vital to ensure data accuracy and compliance with relevant policies.
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Experience in programming languages such as Python or Java for data manipulation, along with SQL proficiency for database management, is highly advantageous.
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The role demands effective collaboration within cross-functional teams and the ability to communicate technical concepts to non-technical stakeholders.
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A proactive approach to problem-solving and a commitment to continuous learning are crucial given the rapid evolution of the data engineering field.
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A Bachelor’s or Master’s degree in a relevant field is required, and having certifications like Google Cloud Certified - Professional Data Engineer is preferred.
PySpark, ETL, SQL, Data Engineering, Python
