Job Description & How to Apply Below
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:
Data Accessibility Engineering Support:
Manager, Data Operations & Management:
As the Manager of Data Accessibility Engineering Support, you will play a critical role in ensuring that enterprise data is secure, discoverable, and accessible for advanced analytics, AI / ML, and operational use. You will oversee the implementation and support of data governance tooling, metadata management, and access controls across cloud-native platforms. This role is hands-on and strategic—ensuring compliance with organizational policies while enabling scalable data accessibility across GCP, AWS, Big Query, Redshift, and other modern data environments.
Who we are looking for:
Primary Responsibilities:
Data Accessibility & Governance Enablement:
- Lead the implementation and support of data accessibility solutions, ensuring efficient access to governed and trusted data assets.
- Oversee data governance tools and platforms (e.g., Collibra) for metadata management, lineage, and policy enforcement.
- Manage and maintain technical metadata and data cataloging frameworks that support enterprise discoverability.
Cloud Platform Integration:
- Design and implement data accessibility frameworks for GCP and AWS environments, with a strong focus on Big Query, Redshift, and cloud-native storage layers (GCS / S3).
- Collaborate with cloud engineering and security teams to enforce fine-grained access controls and data classification.
AI / ML Support & Lifecycle Management:
- Partner with AI / ML teams to support model lifecycle management through reliable access to training and scoring datasets.
- Ensure data quality and accessibility standards are embedded in MLOps workflows and pipelines.
Data Quality, Policy & Compliance:
- Implement and monitor enterprise data quality frameworks to support regulatory compliance and business confidence.
- Develop strategies for reconciliation, validation, and data forensics to resolve data inconsistencies.
- Ensure alignment with organizational data usage policies, privacy standards, and auditability requirements.
Cross-Functional Collaboration & Support:
- Work closely with data stewards, data engineers, data scientists, and compliance teams to continuously improve data operations.
- Provide Tier 2 / 3 support for data accessibility and metadata-related issues.
- Lead efforts to educate teams on data usage best practices, standards, and governance workflows.
Skill:
- 7 to 11 years of experience in data operations, data governance, or data quality engineering roles.
Hands-on experience with:
- Data governance platforms, especially Collibra with recent 3+ years.
- Metadata management, cataloging, and data technical lineage.
- Hands-on experience with Workflow and REST API’s (Groovy and Python) programming languages.
- AI / ML data workflows and supporting structured / unstructured data access for model training and inferencing Preferred [AI Governance].
- Cloud platforms:
Google Cloud Platform (GCP), Amazon Web Services (AWS).
- Data warehouses:
Big Query, Redshift (and / or Snowflake).
- SQL and enterprise-scale ETL / ELT pipelines.
- Strong analytical and problem-solving skills in large-scale, distributed data environments.
- Familiarity with data security, privacy regulations, and compliance standards (e.g., GDPR, CCPA).
- Excellent collaboration and communication skills across technical and non-technical teams.
- Bachelor’s or master’s degree in data science, Information Systems, Computer Science, or a related field.
Preferred Experience:
- Experience in Retail or QSR environments with complex multi-region data access needs.
- Exposure to enterprise data catalogs, automated data quality…
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