Senior Data Engineer

Roles & Responsibilities:

Build & Scale Enterprise Data Platform

• Design, build, and operate a scalable enterprise data platform with reliable batch and real-time data pipelines to power analytics, AI, and business applications.
• Leverage AI across the data engineering lifecycle to accelerate development, improve engineering productivity, and automate data quality, lineage, observability, governance, and platform monitoring.
• Optimize platform performance, reliability, scalability, and cost through modern cloud-native architectures, automation, and engineering best practices.
• Enable self-service data consumption through reusable data products, APIs, and governed datasets that empower Analytics, AI, and business teams.

AI/MLOps & Production AI Platform

• Build and operate a scalable MLOps platform that supports the complete machine learning lifecycle—from feature engineering and model training to deployment, monitoring, and automated retraining.
• Design, deploy, and manage production-grade model serving infrastructure for real-time and batch inference, ensuring high availability, low latency, scalability, and reliability.
• Partner with Data Scientists and AI Engineers to productionize AI/ML solutions by implementing robust deployment pipelines, model governance, monitoring, and operational best practices.

QUALIFICATIONS, EXPERIENCE, & SKILLS

• 5 to 10 years of experience building data platforms using real-time as well as batch-processing big data solutions, including at least 2 years in a senior/lead engineering capacity.
• Proficiency with data modeling, data warehousing, and building ETL/ELT pipelines at scale.
• Exceptional programming skills in SQL, Python, Shell scripting, and Scala/PySpark.
• Hands-on experience with AWS big data technologies (EC2, Redshift, S3, Glue, Kinesis and Lambda); exposure to other cloud platforms (Azure/GCP) is a plus.
• Demonstrated experience using GenAI/LLM tools and frameworks (e.g., prompt engineering, RAG pipelines, LangChain/LlamaIndex, AI coding assistants such as Claude Code or GitHub Copilot, or building custom AI agents) to automate or accelerate engineering workflows.
• Practical understanding of how to apply GenAI agents responsibly to repetitive data engineering tasks, with appropriate guardrails around data quality, security and governance.
• Proficiency with Git/GitHub and modern CI/CD practices.
• Experience with workflow orchestration tools (e.g., Airflow, dbt) and containerization (Docker/Kubernetes) is preferred.
• Strong stakeholder management and the ability to translate business problems into scalable technical solutions.
• Strong match with our value system & DNA.

We offer an opportunity to:

• Help write next growth story for most successful QSR brand and helping become a leading food tech company in the world.
• Build cutting edge, AI-augmented data solutions across a diverse set of problems and see the impact of your work immediately.
• Elevate your core skills & build commercial acumen while working at the frontier of GenAI adoption in data engineering.
• Work with & learn from exceptional talent in a fast-paced, entrepreneurial environment.