Senior Software Engineer

Pearson•Hoboken, NJ

19h•Onsite

About The Position

Pearson is 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, including learner activity, assessments, recommendations, and analytics. The engineer will own end-to-end data workflows, develop scalable batch and streaming pipelines, and contribute to data modeling standards. Key responsibilities also include ensuring data quality, reliability, and security, collaborating with cross-functional teams, mentoring junior engineers, and driving continuous improvement in tooling and practices. The ideal candidate will operate independently on complex problems, influence architectural decisions, and balance long-term platform evolution with short-term delivery needs.

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
Designing and operating data platforms in cloud environments (e.g., AWS‑based data services).
Building ETL/ELT pipelines, orchestration workflows, and data models at scale.
Implementing secure data access, encryption, and governance controls.
Writing maintainable, testable code using modern engineering practices.
CI/CD, infrastructure‑as‑code, and automated deployment of data pipelines.
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.