Rackspace Technology
IT as a Service • Multi-Cloud • Managed Hosting • Managed AWS/Azure/Google Cloud Platform/OpenStack/Alibaba • Managed Private Cloud for VMware/Microsoft/OpenStack
Data Engineering Lead
August 14
Rackspace Technology
IT as a Service • Multi-Cloud • Managed Hosting • Managed AWS/Azure/Google Cloud Platform/OpenStack/Alibaba • Managed Private Cloud for VMware/Microsoft/OpenStack
Description
• Be responsible for the implementation of the FDR and other Data products as defined by business owner and tech product owner. • Lead the Data delivery team in the planning and implementing the FDR and other Data products as per defined timelines & Prudential Engineering framework & policies and transition to Run team post go-live • Work in close collaboration with project sponsors and stakeholders to ensure appropriate definition of project goals, deliverables, and acceptance criteria • Coordinate interactions with the business platforms/entities • Day-to-day coordination on progress, prioritization and problem solving with the Data implementation team. • Provide timely and concise updates to Finance GTP (Global Tech Product) Owner and inputs to the governance committees. • Support the process of documenting business requirements/user stories from stakeholders. • Support prioritization of the requirements catalogue/user story backlog • Ensure good level of both technical & functional documentation for knowledge management & communications for within Engineering & Finance GTP
Requirements
• Minimum 10+ years of relevant experience in IT implementation for Finance domain (MIS, Accounting & Actuarial) • Bachelor's degree in Computer Science, Data Engineering, or related field (Master's degree preferred). • Strong analytical skills, leadership capabilities, effective communication, and problem-solving ability • Deep technical knowledge in data engineering, database management and data warehousing technologies (e.g. SQL, Python, Spark, Azure stack, Databricks) • Strong Data modeling and managing Distributed Computing Platforms for Data Processing, with understanding of frameworks like Hadoop, Spark, or Flink. • Advance knowledge of SQL and writing resource-efficient queries • High expertise with data integration tools, data pipeline orchestration tools, and workflow management systems
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