Senior Software Engineer

Pearson•Hoboken, NJ

1d•Onsite

About The Position

We are seeking a Senior Software Engineer to join our Platform & Data Engineering team. This role involves designing, building, and maintaining cloud-native data pipelines and platforms that support various use cases such as learner activity, assessments, recommendations, and analytics. You will own end-to-end data workflows, develop scalable batch and streaming pipelines, and contribute to data modeling standards. A key aspect of this role is ensuring data quality, observability, and pipeline reliability through monitoring and automated validation. You will also apply Pearson’s data security, privacy, and retention standards, and support production incident analysis. Collaboration with product managers, analytics engineers, data scientists, and platform teams is essential. As a technical mentor, you will guide junior engineers and set best practices. You will also provide technical input into architectural decisions, roadmap planning, and platform modernization. Continuous improvement of tooling, frameworks, and engineering practices is expected, along with evaluating emerging technologies. This is a senior-level position that requires operating independently on complex problems, influencing architectural decisions, and providing technical leadership without direct people management.

Requirements

Collaboration and cross-functional communication
Accountability and ownership of outcomes
Attention to detail and quality
Ethical responsibility and data stewardship
Adaptability in a changing technology landscape
Cloud Computing: Designing and operating data platforms in cloud environments (e.g., AWS-based data services).
Data Engineering: Building ETL/ELT pipelines, orchestration workflows, and data models at scale.
Data Security: Implementing secure data access, encryption, and governance controls.
Software Engineering: Writing maintainable, testable code using modern engineering practices.
DevOps / DataOps: CI/CD, infrastructure-as-code, and automated deployment of data pipelines.
Observability: Monitoring, logging, and alerting for data systems and pipelines
Full-stack development concepts, including integration with Web APIs
Programming languages: Java, Python
AWS cloud services used for data platforms
Datastores: DynamoDB, Aurora DB, MongoDB, RDBMS
CI/CD and operational practices supporting data platforms

Nice To Haves

AI-enabled and ML-adjacent data platform patterns
Automated data quality and intelligent observability
Event-driven and streaming architectures
Advanced data governance and lineage automation

Responsibilities

Design, build, and maintain cloud-native data pipelines and platforms supporting PLS and IAR use cases (e.g., learner activity, assessments, recommendations, analytics).
Own end-to-end data workflows across ingestion, transformation, storage, and serving layers.
Develop scalable batch and streaming pipelines that meet performance, reliability, and data-quality expectations.
Contribute to data modeling standards that support downstream analytics, ML, and reporting needs.
Ensure data quality, observability, and pipeline reliability through monitoring, automated validation, and alerting.
Apply Pearson’s data security, privacy, and retention standards in all platform designs.
Support production incident analysis, root-cause identification, and long-term remediation.
Collaborate with product managers, analytics engineers, data scientists, and platform teams to align data solutions to business goals.
Act as a technical mentor for junior engineers, setting best practices for data engineering and platform development.
Provide technical input into architectural decisions, roadmap planning, and platform modernization initiatives.
Drive continuous improvement in tooling, frameworks, and engineering practices within the PLS / IAR data platform.
Evaluate emerging technologies and patterns to evolve Pearson’s data ecosystem responsibly.