Data Engineer III

Data Engineer III

We are seeking a highly skilled Data Engineer III to help design, build, and scale enterprise data products on Google Cloud Platform (GCP). This role is responsible for developing modern data pipelines, transformation frameworks, semantic data models, and analytics solutions that power business-critical reporting, advanced analytics, and AI-enabled use cases across Pearson. The ideal candidate combines strong data engineering practices with deep expertise in cloud-native data platforms, data modeling, analytics engineering, and platform governance. This role will work across Data Products / Data 360s and other strategic data initiatives utilizing BigQuery, dbt, Looker, Power BI, and modern CI/CD practices.

Key Responsibilities

• Design, build, and maintain scalable data pipelines and ELT workflows on GCP using our DBT platform.
• Develop and support data products using Medallion Architecture (Bronze, Silver, and Gold layers).
• Build and maintain dbt models, reusable transformations, and analytics engineering frameworks.
• Design optimized BigQuery data models, datasets, views, and performance tuning strategies.
• Implement automated data quality, testing, observability, and monitoring solutions.
• Develop and support semantic models for analytics and reporting platforms including Looker and Power BI.
• Partner with analytics engineers, software engineers, QA engineers, architects, and business stakeholders to deliver trusted data products.
• Support CI/CD, release management, automated testing, and deployment processes.
• Implement and maintain data governance, security, privacy, and access control standards.
• Contribute to architecture decisions, platform standards, and engineering best practices.
• Troubleshoot production issues and drive continuous platform improvements focused on reliability, scalability, and operational excellence.

Required Skills & Experience

Technical Skills

• Google Cloud Platform (GCP)
• BigQuery
• dbt Core
• Advanced SQL
• Python
• REST API integration
• Git / GitHub
• CI/CD pipelines
• Data modeling and dimensional modeling
• Data quality and test automation
• Performance optimization and query tuning
• Cloud-native architecture and software engineering practices
• AI Agent Frameworks and Implementations (GCP Gemini)

Analytics & Visualization

• Looker / LookML
• Power BI
• Semantic modeling
• KPI and metric development
• Dashboard optimization and analytics enablement

Engineering Practices

• Agile development methodologies
• DevOps and automation practices
• Infrastructure-as-Code concepts
• Monitoring and observability frameworks
• Technical design and architecture documentation
• Peer reviews and collaborative engineering practices

Preferred Qualifications

• Experience building enterprise-scale data and analytics platforms.
• Experience supporting Marketing, Digital Analytics, Customer, or Advertising data domains.
• Experience implementing Medallion Architecture and data product operating models.
• Experience with orchestration technologies such as Airflow, Cloud Composer, or Cloud Run.
• Understanding of data governance, GDPR, PII management, and enterprise security controls.
• Experience supporting AI, machine learning, and conversational analytics use cases.
• Experience working in highly collaborative cross-functional environments with product, engineering, governance, and business stakeholders.

What Success Looks Like

• Delivers high-quality, production-ready data solutions with minimal supervision.
• Drives improvements in platform reliability, scalability, performance, and maintainability.
• Establishes reusable engineering patterns, frameworks, and best practices.
• Automates manual processes and improves operational efficiency across the platform.
• Partners effectively with stakeholders to translate business requirements into scalable technical solutions.
• Provides technical leadership, mentors fellow engineers, drives engineering best practices, and influences the technical direction of enterprise data products.
• Enables trusted, governed, and scalable data products that accelerate analytics and business decision-making.

Ideal Candidate Profile

A senior-level Data Engineer with strong expertise in GCP, BigQuery, dbt, Python, and analytics engineering who can independently own the end-to-end delivery of enterprise data products while influencing platform architecture, engineering standards, governance practices, and the long-term evolution of Pearson's modern data ecosystem.

Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:

The minimum full-time salary range is between $80,000 - $100,000

This position is eligible to participate in an annual incentive program, and information on benefits offered is here.

Applications will be accepted through 17th August 2026. This window may be extended depending on business needs.