Lead Cloud Data Engineer — GCP Data Platforms

Panera Bread; Georgia; Hybrid; USD 127,000 - 164,000; Full time

Job summary

Panera Bread seeks a hands-on Lead Cloud Data Engineer to design, build, and operate enterprise data platforms on Google Cloud Platform. You will guide data ingestion, streaming, governance, and production operations while mentoring a team of engineers and advancing AI-enabled data capabilities.

The role requires strong leadership, architectural skill, and a proven ability to deliver secure, scalable data solutions across operational and analytical workloads.

Qualifications

• Bachelor’s degree in computer science, Engineering, MIS, or related field (Master’s preferred).
• 5+ years of experience in data engineering, data platform development, and cloud-based data solutions, including demonstrated technical leadership, & team mentoring responsibilities.
• 5+ years of hands-on experience designing, building, and delivering large-scale enterprise data platforms and pipelines on Google Cloud Platform (GCP) or similar.
• 4+ years of hands-on experience with BigQuery, including SQL routines performance tuning, query optimization, workload management, partitioning, clustering, materialized views, and query optimization.
• 4+ years of hands-on experience with Dataflow (Apache Beam), Dataproc (Managed Apache Spark), Python, PySpark, SQL, and Pandas for large-scale batch, streaming and real-time data processing.
• Proven experience managing cloud infrastructure consumption, capacity planning, environment provisioning, and cloud cost optimization, including reservation and usage optimization strategies.
• Deep expertise with Cloud Storage, Cloud SQL, Datastore, DataStream (CDC), Pub/Sub, and event-driven architectures supporting operational, analytical, real-time, and streaming data integration.
• Hands-on experience with Cloud Run, Cloud Functions, Compute Engine, App Engine and serverless computing architectures supporting batch, real-time, and streaming data processing solutions.
• Advanced experience designing and implementing workflow orchestration and automation solutions using Cloud Composer (Managed Airflow DAG), Cloud Workflows, Cloud Scheduler, and serverless execution frameworks.
• Experience implementing Infrastructure as Code (IaC), CI/CD pipelines, and automated deployment practices using Terraform Cloud and GitLab.
• Demonstrated experience implementing Data Governance, Data Quality, Metadata Management, Master and Reference Data Management, and Data-as-a-Product principles within enterprise data ecosystems.
• Experience working with enterprise data ecosystems and related technologies, including Oracle EDW, PostgreSQL, Kafka, Power BI, and Looker.

Responsibilities

• 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 and operational processes.
• Collaborate with enterprise architects, product owners, business analysts, BI developers, data engineers and cross-functional stakeholders to align data engineering solutions with business priorities, governance requirements, and data product delivery goals.
• Optimize platform performance, scalability, reliability and cost efficiency through effective use of cloud-native technologies, automation, and engineering best practices.
• Evaluate and leverage emerging AI, Generative AI, and agentic AI capabilities to enhance data engineering productivity, automate operational processes, improve data quality and data cataloging, and accelerate data solution delivery.

Skills

BigQuery

Dataflow

Dataproc

Python

PySpark

SQL

Pandas

GCP

Cloud Storage

Education

Bachelor’s degree in computer science, Engineering, MIS, or related field

Tools

Terraform

GitLab

Cloud Composer

Job description

Panera Bread seeks a hands-on Lead Cloud Data Engineer to design, build, and operate enterprise data platforms on Google Cloud Platform. You will guide data ingestion, streaming, governance, and production operations while mentoring a team of engineers and advancing AI-enabled data capabilities.

The role requires strong leadership, architectural skill, and a proven ability to deliver secure, scalable data solutions across operational and analytical workloads.