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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:
Distributed Systems, Data Architecture, Software development best practices, Data engineering best practices, Big data platforms, Data pipelines, Data Product Lifecycle Standards and Practices
Skills:
Distributed Systems, Data Architecture, Software development best practices, Data engineering best practices, Big data platforms, Data pipelines, Data Product Lifecycle Standards and Practices
Skills:
Distributed Systems, Data Architecture, Software development best practices, Data engineering best practices, Data pipelines, Data Product Lifecycle Standards and Practices
Skills:
Ml, Data Architecture, Metadata lineage, Business intelligence, Data readiness, Ai, Data onboarding, Access management, Semantic layers, Data quality governance
Skills:
Artificial Intelligence and ML?based threat detection and automation tools, Data interpretation and feature?analysis capabilities for threat?analytics models, Programming or scripting PHP Python Javascript, Data enrichment pipelines, Digital forensics tools and techniques, Incident response including major incident response leadership, Technical writing and communication, Cyber defense frameworks NIST ISO CIS, Automation platforms SOAR, SIEM tools Splunk Sentinel, Understanding of ML model behavior
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