This role will be accountable for building and maintaining scalable data pipelines from source systems. The Data Engineer will ensure the availability, reliability, and performance of data products by integrating raw data from various sources. Key responsibilities include data modeling, ETL (Extract, Transform, Load) development, and ensuring data quality and security. This role will be accountable for data coming in from 5-10+ source systems.
Design and Execute
• Partner with data product managers to gather and deliver data pipelines.
• Design ETL solutions including data quality, data security, and data pipeline resiliency.
• Execute ETL solutions including data security, data quality and performance requirements.
Data Extraction, Load and Transformation
• Design, build and implement ELT pipelines to efficiently ingest and transform data from a wide variety of data sources and deliver datasets that meet business requirements.
• Optimize performance for large datasets and data workflows for performance, scalability, and reliability to support business needs.
• Develop and maintain scalable data pipelines leveraging Azure Synapse, PySpark, APIs, and SQL & performing advanced data cleaning, transformation, and manipulation to ensure high-quality, and reliable data flows.
• Implement CI/CD processes to streamline and automate data pipelines deployment
• Apply data validation frameworks (Great Expectations, Fabric-native tools) to maintain accuracy
• Utilize partitioning, indexing, clustering strategies to enhance query performance
Process Improvement, Performance and Cost optimization tuning
• Collaborate with Data Science, AI, and Data product teams to optimize performance and cost effectiveness of their solutions.
• Identify and support the design of internal process improvements, including automating manual processes, optimizing data product delivery, and redesigning solutions for enhanced scalability.
• Implement solution adjustments to improve performance and cost-effectiveness of data products.
Issue Resolution and Support
• Monitor and troubleshoot the data pipelines proactively, which includes leading the support of data-related product pipeline issues to resolve data errors.
• Provide expert-level support and guidance to data teams across the Enterprise.
Desired Candidate Profile:
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UDIT - University of Design, Innovation and Technology
UDIT - University of Design, Innovation and Technology