LexisNexis Legal & Professional is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision-making and outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® ...
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As a Data Engineer at LexisNexis Intellectual Property (LNIP), you’ll contribute to building and maintaining our next-generation Strategic Data Platform. This platform ingests, enriches, and transforms global patent and IP-related data to power key products like PatentSight+, as well as a growing ecosystem of internal tools and customer-facing solutions.
In this early-career role, you will collaborate with senior engineers and technical leads to design robust data pipelines, apply engineering best practices, and support the delivery of high-quality data through modern platforms such as Databricks, APIs, and event-driven systems. You'll gain practical experience working at scale while contributing to the delivery of data directly to customers and systems across the organisation.
Key Responsibilities:
Contributing to the development and maintenance of data pipelines using Python, PySpark, and Databricks
Supporting the delivery of enriched datasets to customers via Databricks, RESTful APIs, and event-driven delivery mechanisms (e.g., Kafka or similar)
Assisting in data ingestion, transformation, and enrichment across the medallion architecture (bronze → silver → gold)
Collaborating with cross-functional teams, including engineers, data analysts, and product managers
Participating in code reviews, unit testing, and documentation to ensure high code quality and maintainability
Troubleshooting and debugging data issues across development and production environments
Following and contributing to internal best practices around data engineering and software development
Continuously developing technical skills and understanding of the business domain
Requirements:
Hands-on experience in a software/data engineering role.
Proficiency in Python and working knowledge of PySpark or similar distributed data frameworks.
Familiarity with Databricks or a strong interest in learning and working with the platform.
Understanding of data delivery patterns, including REST APIs and event-driven architectures.
Experience with SQL and structured data manipulation.
Familiarity with version control systems (e.g., Git).
Strong problem-solving mindset and willingness to learn from feedback.
Good communication skills and ability to work in a team setting.
Nice to Have:
Exposure to cloud platforms like AWS, Azure, or GCP.
Experience working with large-scale or open datasets.
Familiarity with medallion architecture or similar data lake patterns.
Understanding of data quality principles and CI/CD pipelines for data workflows.
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