Data Engineer – Global Data Team
Role Overview
Data Engineer with 6–10 years of hands-on experience
cloud-based data platforms, lakehouse architectures, and distributed data pipelines
The ideal candidate is a hands-on engineer who enjoys solving complex data challenges, building reusable engineering frameworks, improving platform reliability, and enabling data teams through scalable self-service capabilities.
Key Responsibilities
-
Design, develop, and maintain
scalable ETL/ELT data pipelines
supporting enterprise data products and analytics.
-
Build and optimize data solutions using
Google BigQuery, GCS, Cloud Composer/Airflow, dbt, Python, and SQL
.
-
Develop robust data ingestion and transformation pipelines from databases, SaaS applications, APIs, files, and other enterprise data sources.
-
Implement and maintain
API integrations and data ingestion frameworks
for cloud and enterprise applications.
-
Develop reusable
dbt models, transformation frameworks, and data quality processes
.
-
Build and manage workflow orchestration using
Apache Airflow / Google Cloud Composer
.
-
Work with CDC and data ingestion technologies such as
Informatica CDC, Airflow, Composer, dbt, or similar platforms
.
-
Design and implement
modern data lakehouse architectures
using BigQuery and related cloud technologies.
-
Optimize data pipelines and BigQuery workloads for
performance, scalability, reliability, and cost efficiency
.
-
Implement engineering best practices including
CI/CD, version control, automated testing, deployment automation, and DevOps practices
.
-
Contribute to
data observability and monitoring frameworks
, including pipeline health, data quality, SLA monitoring, and operational metrics.
-
Partner with data architects, analysts, data scientists, product teams, and business stakeholders to deliver high-quality data solutions.
-
Contribute to
data governance, metadata management, lineage, and data discovery
capabilities.
-
Explore and implement
AI/ML and GenAI capabilities
to improve data engineering workflows, automation, data discovery, and developer productivity.
-
Troubleshoot complex data pipeline and platform issues and drive root-cause analysis and long-term improvements.
-
Help establish engineering standards, reusable frameworks, and best practices across the Global Data organization.
-
Champion
self-service data capabilities
that improve the experience of data consumers and engineering teams.
Required Skills and Experience
-
6–10 years of professional experience in Data Engineering, Data Platform Engineering, or a closely related field.
-
Strong hands-on experience developing
ETL/ELT pipelines
and data transformation workflows.
-
Strong programming experience with
Python
.
-
Strong SQL skills, including experience with complex queries, optimization, and large-scale data processing.
-
Hands-on experience with
dbt
and modern data transformation practices.
-
Hands-on experience with
Apache Airflow and/or Cloud Composer
for workflow orchestration.
-
Strong experience working with
Google BigQuery
or a comparable cloud data warehouse.
-
Experience with
Google Cloud Storage (GCS)
and cloud-based data platforms.
-
Experience building and integrating data pipelines using
REST APIs and other data integration mechanisms
.
-
Strong understanding of data modeling, data warehousing, and modern
lakehouse architectures
.
-
Experience working with large-scale distributed data pipelines and production data environments.
-
Experience with
Git, CI/CD, automated testing, and DevOps practices
.
-
Strong problem-solving and analytical skills with the ability to troubleshoot complex data engineering issues.
-
Ability to work effectively in a
global, collaborative, and cross-functional environment
.
-
Excellent written and verbal communication skills.
Preferred Skills
-
Strong experience with
GCP data services
, particularly BigQuery, GCS, Cloud Composer, and related services.
-
Experience with
Informatica CDC / Mass Ingestion
or other change-data-capture technologies.
-
Experience with additional cloud platforms such as
AWS or Azure
.
-
Experience with
data governance and metadata management platforms
, such as Collibra or similar tools.
-
Experience implementing
data observability frameworks and tools
.
-
Experience with data quality, lineage, cataloging, and metadata management.
-
Experience with
Terraform or Infrastructure as Code
.
-
Experience with containerization and modern DevOps technologies.
-
Exposure to
AI/ML and GenAI technologies
, including using LLMs to automate or enhance data engineering workflows.
-
Experience building
self-service data platforms, reusable engineering frameworks, or data products
.
-
Experience with data security, privacy, access controls, and enterprise data governance.
-
Experience optimizing cloud data platforms for
performance and cost
.
What You Will Bring
-
A
hands-on engineering mindset
with a passion for building production-grade data solutions.
-
Strong ownership and accountability for the reliability and quality of data pipelines and platforms.
-
A continuous-improvement mindset and enthusiasm for
automation, standardization, and reusable frameworks
.
-
Ability to simplify complex technical problems and develop scalable solutions.
-
Curiosity and willingness to learn and adopt emerging technologies, particularly
AI/ML and GenAI
.
-
A strong focus on
platform reliability, data quality, observability, and developer/user experience
.
-
Ability to collaborate effectively with globally distributed engineering, architecture, product, and business teams.
-
Strong communication skills and the ability to explain complex technical concepts to both technical and non-technical audiences.
-
Passion for building
self-service capabilities
that enable teams to discover, access, understand, and use data effectively.
Why Join Us
-
Build and scale next-generation cloud data platforms
powering enterprise-wide analytics, data products, and AI initiatives.
-
Join
Pearson
, a global leader transforming lives through learning and innovation.
-
Work hands-on with
GenAI, AI-powered data engineering, and intelligent automation
.
-
Shape the future of
self-service data, data products, governance, and observability at scale
.
-
Collaborate with high-performing
global data and technology teams
solving real-world, high-impact problems.
-
Work with modern technologies across
GCP, BigQuery, dbt, Airflow/Composer, Python, and AI/ML
.
-
Have the opportunity to influence
data engineering standards, architecture, and platform strategy
.
-
Accelerate your career in a
fast-evolving, innovation-driven global data ecosystem
.
Who we are:
At Pearson, our purpose is simple: to help people realize the life they imagine through learning. We believe that every learning opportunity is a chance for a personal breakthrough. We are the world's lifelong learning company. For us, learning isn't just what we do. It's who we are. To learn more: We are Pearson.
Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
If you are an individual with a disability and are unable or limited in your ability to use or access our career site as a result of your disability, you may request reasonable accommodations by emailing TalentExperienceGlobalTeam@grp.pearson.com.
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