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About McDonald's:
One of the world's largest employers with locations in more than 100 countries, McDonald's Corporation has corporate opportunities in Hyderabad. Our global offices serve as dynamic innovation and operations hubs, designed to expand McDonald's global talent base and in-house expertise. Our new office in Hyderabad will bring together knowledge across business, technology, analytics, and AI, accelerating our ability to deliver impactful solutions for the business and our customers across the globe.
Position Summary:
Looking to hire a Senior Engineer at the G5 level who has a deep understanding of Data Product Lifecycle, Standards and Practices. Will be responsible for building scalable and efficient data solutions to support the Finance, Franchising & Development function with a specific focus on the Finance Analytics product and initiatives. As a Senior Engineer, you will collaborate with data scientists, analysts, and other cross-functional teams to ensure the availability, reliability, and performance of data systems. Leads initiatives to enable trusted financial data, supports decision-making, and partners with business and technology teams to align data capabilities with strategic finance objectives. Expertise in cloud computing platforms, technologies and data engineering best practices will play a crucial role within this domain.
Who we're looking for:
Primary Responsibilities:
Skill:
Work location: Hyderabad, India
Work pattern: Full time role.
Work mode: Hybrid.
Job ID: 150482673
Skills:
operational readiness , Software Delivery, Scrum, data pipelines, mobile backend, agile delivery, cloud-native software products, Compliance, release governance, program management, ML services, Kanban, Engineering Operations
Skills:
Software Delivery, Scrum, risk management, data pipelines, mobile backend, cloud-native software products, agile delivery, delivery governance, Capacity Planning, Stakeholder Management, program management, ML services, Kanban, Engineering Operations
Skills:
data engineering , snowflake , Gcp, Data Architecture, Databricks, AWS, investment data domains, Data APIs
Skills:
Spark SQL, Performance Tuning, Pyspark, SQL Server, Azure Synapse, Databricks, Microsoft Azure, CI CD for data pipelines, Lakehouse concepts, Troubleshooting, Azure SQL SQL Managed Instances, Infrastructure-as-code concepts, Git-based development
Skills:
Distributed Systems, Data Architecture, Software development best practices, Data engineering best practices, Big data platforms, Data pipelines, Data Product Lifecycle Standards and Practices