Sr Manager, Data Architecture

Department Overview

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. 

Duties

• Design, govern, and maintain enterprise-grade conceptual, logical, and physical data models, establish canonical structures for master, transactional, and reference data across global markets, and manage their lifecycle and versioning in Erwin Data Modeler. 
• Oversee schema evolution, partitioning, clustering, and materialized view strategies in Google BigQuery. 
• Architect resilient end-to-end ELT / ETL pipelines for batch and low-latency streaming workloads, and define standard Apache Airflow (Astronomer) DAG patterns with reusable operators, dependency handling, and automated failure recovery. 
• Enforce software engineering rigor and quality through Git, GitHub Actions reusable workflows, Terraform, Liquibase schema migrations, automated testing, environment promotion, data observability, and data quality harnesses (dbt tests, Dataplex data quality, reconciliation checks). 
• Drive performance engineering and FinOps through SQL tuning, BigQuery slot reservations, data skew diagnosis, and compute and storage cost optimization. 
• Champion medallion layering (raw, bronze, silver, gold, and a semantic / KPI layer) and Kimball dimensional modeling, and set dbt coding standards and package structures. 
• Run Architecture Review Board and cross-functional architecture sessions with approval authority on physical schemas, ingestion blueprints, and pipeline code before deployment. 
• Maintain automated lineage, data dictionary synchronization, enterprise catalog integration (Dataplex / Knowledge Catalog), and Data Interface Contracts. 
• Design ingestion frameworks for vendor files, OAuth2 APIs, Confluent Kafka streams, and existing Talend jobs, lead Talend-to-dbt modernization, and design serving layers including consumption datamarts and REST APIs. 
• Translate analytical and AI / ML use cases into production data products with portfolio and product leads, and ensure consumption across Tableau, MicroStrategy, and web applications. 
• Enforce zero-trust access with column-level access control, row-level security, policy tags, authorized views, and dynamic data masking, using PAM-gated role access and Workload Identity Federation, in line with GDPR, CCPA / CPRA, and corporate security frameworks. 
• Design and own the semantic / KPI layer and governed metric definitions, so that restaurant performance KPIs are defined once and reused across BI, applications, and AI, with ontology design where it adds value. 
• Build GenAI data foundations on Vertex AI, Gemini, and Google ADK, including grounding AI agents on governed data, embeddings and vector search, and LLM output evaluation. 
• Lead the adoption of new restaurant location and hierarchy master data across restaurant data products. 
• Advise leadership on data architecture roadmaps and cloud investments, drive architectural readiness in PI planning, mentor lead engineers, modelers, and analytics engineers, and drive adoption of AI-assisted engineering with standards for review, security, and responsible use. 

Qualifications

• 10+ years of progressive experience in data engineering, data architecture, and software engineering, with at least 3–4 years in an architectural or engineering leadership role. 
• Proven track record designing, building, and operating multi-terabyte to petabyte-scale data lakes and enterprise warehouses in multi-region cloud environments. 
• Deep GCP data ecosystem expertise: BigQuery, Cloud Storage, Dataflow / Dataproc, Pub/Sub, Cloud Run, Dataplex, and IAM. 
• Advanced Python (OOP, concurrency, Pandas / Polars) and production-grade SQL (window functions, analytical transforms, execution plan tuning). 
• Expert data modeling with Erwin Data Modeler or ER/Studio across relational, dimensional (star / snowflake), and lakehouse paradigms. 
• Hands-on mastery of dbt Core and Apache Airflow (Astronomer). 
• Practical expertise with Docker, Git workflows, GitHub Actions, and Terraform. 
• Demonstrated experience with automated data testing, schema contract validation, and pipeline health monitoring. 
• Holistic systems thinking, moving from enterprise architecture roadmaps to Python code reviews, SQL profiling, and CI/CD debugging. 
• Influential communication and pragmatic governance through developer enablement, automated guardrails, and templates. 
• Hands-on use of AI coding assistants to improve engineering productivity, with standards for review and responsible use. 
• Hands-on semantic engineering: semantic layers and metric stores (tool-agnostic). 
• GenAI experience building LLM applications on Vertex AI / Gemini and Google ADK, including RAG, embeddings, vector databases, prompt and agent design, LLM evaluation, and responsible AI guardrails. 
• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems, or a related quantitative field. 
• Preferred: ontology design; knowledge graph design and querying (Neo4j or Spanner Graph, with Cypher, GQL, or SPARQL) and entity resolution; Google Professional Data Engineer, Google Professional Cloud Architect, Google Professional ML Engineer, Databricks, or TOGAF certification; distributed streaming and event-driven architectures on Confluent Kafka or Pub/Sub; Gravitee API gateway; agent frameworks (Google ADK, Vertex AI Agent Builder, LangGraph) and Model Context Protocol (MCP); Talend; and PySpark. 
• Good to know: reverse ETL tooling and Liquibase.