Lead Cloud Data Engineer

Lead Cloud Data Engineer

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Panera Bread is more than just a bakery-café; it’s a digital leader reshaping the quick-service restaurant industry. With over half of our sales generated through digital channels, Panera has led innovations like Rapid Pick-Up®, drive-thru ordering, geofencing for mobile pickup, and personalized loyalty programs. We’re transforming and building best-in-class omnichannel dining at scale, merging hospitality with advanced technology to deliver seamless experiences for millions of guests each week. A visionary technology leader ready to design systems that handle billions of transactions, lead top-performing teams, and shape the future of digital dining, Panera offers the opportunity to make a significant industry impact. Join us & help revolutionize & transform the way world experiences food.

Position Summary

We are seeking a highly skilled, hands-on Lead Cloud Data Engineer to lead the design, development, delivery, and operational excellence of our enterprise data platforms on Google Cloud Platform (GCP). This role serves as a critical bridge between enterprise architecture and solution delivery, translating strategic data architecture into scalable, secure, reliable, and production-ready data solutions that support both operational and analytical business needs. The successful candidate will provide hands‑on development support, technical leadership across the full data lifecycle, including data ingestion, integration, streaming, transformation, orchestration, governance, retention and production operations. Combining deep technical expertise with strong leadership and mentoring capabilities, this individual will lead a team of data engineers, establish engineering best practices, drive delivery excellence, and ensure the performance, scalability, reliability, security, and cost efficiency of enterprise data solutions.

As a data engineer, you will partner closely with architects, product owners, business stakeholders, and platform engineering teams to deliver high‑impact data products and capabilities that enable strategic business outcomes. The role requires exceptional problem‑solving skills, a passion for engineering excellence, and a proven ability to drive architectural consistency, code quality, automation, and operational maturity across the data ecosystem. The ideal candidate will also stay at the forefront of emerging AI, Generative AI, and agentic technologies, identifying opportunities to improve engineering productivity, automation, data quality, and overall platform efficiency.

Duties & Responsibilities

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• Design, develop, test and deploy large scale enterprise data engineering pipelines and data processing ETL/ELT routines in Google Cloud data platform

  
  

• Lead, mentor, and develop a team of data engineers, fostering a culture of technical excellence, collaboration, accountability, innovation, and continuous learning.

  
  

• Drive the design, development, and deployment of scalable batch, real-time, and streaming data pipelines aligned with enterprise architecture standards, cloud well‑architected principles, and business objectives.

  
  

• Translate enterprise data architecture patterns into practical, secure, scalable, and production‑ready solutions that support operational, analytical, and data product workloads.

  
  

• Establish, enforce, and continuously improve engineering best practices, including solution design reviews, code reviews, reusable frameworks and design patterns, modular development, testing standards, CI/CD practices, release management and deployment governance.

  
  

• Design and implement enterprise workflow orchestration, scheduling, and automation solutions to enable reliable, efficient, and resilient data processing across the data ecosystem.

  
  

• Define and promote reusable architectural patterns, frameworks, and standards that improve solution consistency, maintainability, scalability, and operational efficiency.

  
  

• Ensure all solutions adhere to enterprise data governance, data quality, security, privacy‑by‑design, regulatory compliance, and Data‑as‑a‑Product principles.

  
  

• Drive the evaluation, adoption, and implementation of emerging technologies that create measurable business value and improve the effectiveness of the data engineering platform.

  
  

• Own and lead production operations, including release management, monitoring, observability, incident response, root cause analysis (RCA), problem management, and continuous improvement.

  
  

• Partner closely with support and operations teams to ensure platform stability, reliability, resiliency, and compliance with enterprise change management