About the Team
Our team is dedicated to unlocking the rich knowledge embedded within Elsevier s content through our semantic data platform; this empowers researchers, clinicians, and innovators worldwide to gain new insights, make informed decisions, and accelerate progress across research, healthcare, and life sciences. We lead the ongoing transformation of Elsevier s vast, unstructured information into richly interconnected knowledge graphs that capture the full depth and nuance of scientific meaning. Through our dynamic knowledge discovery platform, we combine graph-powered agentic AI with advanced search technologies to deliver contextually relevant, trustworthy, and precise answers.
As part of the Data Engineering team, you ll contribute to the systems and infrastructure that fuel this mission. This role focuses on building reusable platform components and delivering Data Products that enable scalable, reliable, and high-performance data solutions across Elsevier s ecosystem.
The Role
We are looking for a Senior Software Engineer II who can lead the design and implementation of complex systems, mentor team members, and contribute to our evolving data engineering architecture. You ll work on large-scale data pipelines, orchestration frameworks, and services that support our data products collaborating closely with product, platform, and other engineering teams to deliver impactful, high-quality solutions.
Responsibilities
- Design and develop scalable data processing workflows and microservices using Spark , Spark Streaming , and Airflow .
- Write modular, testable code in Python or Scala , aligned with software engineering best practices.
- Lead implementation of system components that span multiple services and modules.
- Diagnose and resolve complex technical issues in distributed data systems.
- Participate in architecture discussions, design reviews, and engineering rituals.
- Develop and maintain data models to support analytical and operational use cases.
- Collaborate with cross-functional stakeholders to translate requirements into engineering solutions.
- Contribute to mentoring and onboarding of junior engineers.
- Champion continuous improvement and knowledge-sharing across the team.
What We re Looking For
- 5+ years of professional experience in software or data engineering.
- Proven track record building and optimizing large-scale batch and streaming data systems.
- Proficiency with Spark , Spark Streaming , Airflow , and either Python or Scala .
- Deep understanding of distributed system design, data modeling, and performance optimization.
- Strong experience with test-driven development and CI/CD practices.
- Ability to independently drive technical outcomes from problem to deployment.
- Familiarity with Agile or other iterative development methodologies.
Nice to Have
- Exposure to graph-based data models or knowledge graph architecture.
- Experience building internal platforms or reusable engineering components.
- Knowledge of observability best practices for data systems (e.g., logging, metrics, alerts).
Career Progression
This role provides a strong foundation for advancement to
- Software Engineering Lead overseeing larger domains and mentoring broader teams.
- Principal Software Engineer driving architecture and cross-team technical strategy.
There are also opportunities for horizontal moves into Systems Engineering, Program Management, or Quality Engineering depending on your career interests.
Why Elsevier
At Elsevier, you ll work on systems that underpin some of the world s most critical research and innovation workflows. Our products and platforms are used by scientists, clinicians, and experts in over 180 countries and you ll be part of the team responsible for building the data infrastructure that powers them.
You ll be supported by a culture that values engineering quality, autonomy, and deep technical exploration. We are actively transforming legacy systems and pioneering modern approaches to data and knowledge representation at scale.
Work Environment
This is a hybrid role. Our teams operate in a flexible hybrid work model, combining in-person collaboration with remote flexibility. You ll be expected to participate in regular team meetings and engineering rituals in line with your team s cadence.
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