Data Engineer II

Are you passionate about building scalable, high-performance data-driven applications? Do you enjoy turning complex datasets into robust, reliable systems that deliver real business impact?

The Search and AI Platform supports core products such as Scopus, ScienceDirect, and AI driven research solutions. As a Junior Data Engineer, you will work hands on building and maintaining data pipelines and services, growing your skills alongside experienced engineers in a collaborative team environment.

This position is suited for an early career professional with 2-3 years of experience who will contribute to research, design, and software development assignments within a software functional area or product line. Working under the guidance of senior team members, this position supports project delivery by participating in planning discussions and following established methodologies. The role involves contributing to software design at the component level, learning and applying best practices through hands on work and code reviews, and developing communication skills by engaging with team members to understand requirements and translate them into solutions.

• Building and maintaining data pipelines and services under the guidance of senior engineers.
• Working with technologies such as Python, SQL, Spark, and Airflow to process large-scale data.
• Using AI assisted development tools such as Claude Code, Copilot, or Codex as part of your workflow.
• Contributing to ELT and ETL pipelines for data ingestion into search, graph, and relational data stores.
• Writing clean, well tested code and learning best practice software engineering principles, including CI/CD.
• Contributing to cloud infrastructure on AWS, with support from the wider team.
• Participating in code reviews and actively seeking feedback to accelerate your growth.
• Collaborating with product, platform, and data science teams to deliver business value.

• 2-3 years of commercial experience as a data or software engineer, including some exposure to production systems.
• Proficiency in Python, SQL, and Spark.
• Basic understanding of data engineering concepts including batch processing and relational databases.
• Some hands on experience with data pipeline tools; exposure to Spark or Airflow is a plus.
• Familiarity with cloud platforms (preferably AWS); production experience is not required.
• A collaborative mindset and willingness to learn and communicate openly within the team.
• Nice to have: Exposure to data lake concepts or formats such as Delta Lake or Iceberg.
• Any experience with search, graph, or large scale analytical databases.
• Interest in AI, machine learning pipelines, or embedding generation.

Elsevier is an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.