Lead Cloud Data Engineer

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

Benefits offered by this job

Hybrid work arrangement

Job summary

Panera Bread Company seeks a Lead Cloud Data Engineer to design, build, and operate enterprise data platforms on Google Cloud Platform. You’ll bridge architecture and delivery, delivering scalable, secure, production-ready data solutions for operational and analytical needs.

You will lead a team of data engineers, set engineering standards, and own production operations, monitoring, governance, and cost optimization while advancing AI-enabled data capabilities to boost productivity and platform

Qualifications

• Bachelor's degree in computer science, engineering, MIS, or related field.
• 5+ years of experience in data engineering and cloud-based data platforms.
• 5+ years designing and delivering large-scale enterprise data 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.
• Experience with cloud cost optimization and IaC (Terraform, CI/CD).
• Experience with data governance, data quality, metadata management, and Data-as-a-Product principles.
• Strong communication and leadership skills.

Responsibilities

• Design, develop, test, and deploy large-scale enterprise data pipelines on the GCP data platform.
• Lead and mentor a team of data engineers, fostering technical excellence and collaboration.
• Drive scalable batch, real-time, and streaming data pipelines aligned with enterprise standards.
• Translate enterprise data architecture patterns into secure, production-ready solutions.
• Establish engineering best practices, code reviews, CI/CD, and deployment governance.
• Design and implement enterprise workflow orchestration and automation solutions.
• Promote reusable architectural patterns and standards for maintainability and cost efficiency.
• Ensure governance, quality, security, and Data-as-a-Product principles are applied.
• Evaluate emerging AI capabilities to enhance productivity and platform efficiency.
• Own production operations, including monitoring, incident response, RCA, and continuous improvement.
• Collaborate with architects, product owners, and stakeholders to align data solutions with business goals.

Skills

Leadership

Mentoring

Problem solving

Communication

Cloud data engineering

Education

Bachelor's degree in Computer Science, Engineering, MIS, or related field

Tools

BigQuery

Dataflow

Dataproc

Python

PySpark

SQL

Pandas

Cloud Composer

Terraform

GitLab

Job description

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

• - 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.

Qualifications

Education:

• - Bachelor’s degree in computer science, Engineering, MIS, or related field (Master’s preferred).

Experience:

• - 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 SQLroutines 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.
• - Strong analytical, problem-solving, and troubleshooting skills, with the ability to resolve complex technical challenges in large-scale, production-grade environments.
• - Excellent communication and stakeholder management skills, with the ability to influence technical and business teams and effectively communicate complex concepts with diverse audiences.
• - Working knowledge of cloud security, networking, and governance, including IAM, VPC networking, firewall management, observability, logging, Data Loss Prevention (DLP), Dataplex (Knowledge Catalog), and enterprise data catalog’ing practices.
• - Working knowledge of AI and agentic development capabilities, including Vertex AI, Google Gemini Enterprise, Agent Studio, Model Garden, Agent Development Kit (ADK), and MCP integrations a plus.

Working Conditions

Hybrid work environment with onsite presence required in Newton MA/or St Louis, MO

Physical Requirements

Regularly required to sit, stand, talk, hear, and use hands and fingers to operate keyboards and devices.

Direct Reports

No. Train and Mentor Junior engineers, and contract workers

Additional Description :

Competitive salary $127,461 - $163,660 annually.