Lead Cloud Data Engineer

Panera Bread

. Design, develop, test, and deploy large-scale enterprise data engineering pipelines and ETL/ELT routines on Google Cloud .

Posted 10/27/2026full-timeUnited StatesSenior💰 $127,461 - $163,660 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing large-scale enterprise data engineering solutions on Google Cloud, with a strong focus on data governance, quality, and security. Proven ability to lead and mentor teams while optimizing data pipeline performance and ensuring adherence to best practices.

Highest-signal resume keywords

Google Cloud Platform (GCP)Data EngineeringBigQueryDataflow (Apache Beam)CI/CD Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonPySparkData GovernanceData QualityInfrastructure as CodeTerraformCloud ComposerCloud WorkflowsCloud Scheduler

Soft Skills

Analytical SkillsProblem-SolvingCommunicationStakeholder ManagementTeam Leadership

Tools & Technologies

Cloud StorageCloud SQLDatastorePub/SubCloud FunctionsCompute EngineApp EngineOracle EDWPostgreSQLPower BI

Industry Keywords

Data-as-a-ProductEvent-Driven ArchitecturesObservabilityData CatalogingCloud Cost Optimization

Tech Stack

Tools & technologies

ApacheBigQueryCloudETLGoogle Cloud PlatformKafkaOraclePandasPostgresPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Design, develop, test, and deploy large-scale enterprise data engineering pipelines and ETL/ELT routines on Google Cloud
Lead, mentor, and develop a team of data engineers
Drive scalable batch, real-time, and streaming data pipeline development aligned with enterprise architecture and business objectives
Translate enterprise data architecture patterns into secure, scalable, production-ready solutions
Establish and improve engineering best practices, including design reviews, code reviews, reusable frameworks, testing standards, CI/CD, release management, and deployment governance
Design and implement workflow orchestration, scheduling, and automation solutions
Define reusable architectural patterns, frameworks, and standards
Ensure adherence to data governance, data quality, security, privacy, regulatory compliance, and Data-as-a-Product principles
Evaluate and adopt emerging technologies that improve data engineering effectiveness
Lead production operations, including release management, monitoring, observability, incident response, root cause analysis, problem management, and continuous improvement
Partner with support and operations teams on platform stability, reliability, resiliency, and change management
Collaborate with architects, product owners, business analysts, BI developers, data engineers, and stakeholders
Optimize platform performance, scalability, reliability, and cost efficiency
Evaluate and leverage AI, Generative AI, and agentic AI capabilities to improve productivity, automation, data quality, cataloging, and delivery

Requirements

What you’ll need

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 technical leadership and team mentoring
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, and materialized views
4+ years of hands-on experience with Dataflow (Apache Beam), Dataproc (Managed Apache Spark), Python, PySpark, SQL, and Pandas
Experience managing cloud infrastructure consumption, capacity planning, environment provisioning, and cloud cost optimization
Expertise with Cloud Storage, Cloud SQL, Datastore, DataStream (CDC), Pub/Sub, and event-driven architectures
Experience with Cloud Run, Cloud Functions, Compute Engine, App Engine, and serverless computing architectures
Advanced experience with Cloud Composer, Cloud Workflows, Cloud Scheduler, and serverless execution frameworks
Experience implementing Infrastructure as Code, CI/CD pipelines, and automated deployment practices using Terraform Cloud and GitLab
Experience implementing Data Governance, Data Quality, Metadata Management, Master and Reference Data Management, and Data-as-a-Product principles
Experience with Oracle EDW, PostgreSQL, Kafka, Power BI, and Looker
Strong analytical, problem-solving, and troubleshooting skills
Excellent communication and stakeholder management skills
Working knowledge of cloud security, networking, governance, IAM, VPC networking, firewall management, observability, logging, DLP, Dataplex, and enterprise data cataloging
Working knowledge of Vertex AI, Google Gemini Enterprise, Agent Studio, Model Garden, ADK, and MCP integrations is a plus
Regularly required to sit, stand, talk, hear, and use hands and fingers to operate keyboards and devices