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LexisNexis Legal & Professional is seeking a Data Engineer to design, build, and maintain scalable data pipelines and platforms for Global Operations Metrics & Technology Support. You will enable high‑quality analytics and AI‑ready data while improving automation, performance, and scalability.
You will collaborate with technical and business partners to translate data needs into practical solutions, document requirements, and support data-driven initiatives.
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Johannesburg HB South Africa
Cape Town
Durban HB South Africa
Full time
R
Data Engineer - Global Operations Metrics & Technology Support
About the Business
LexisNexis Legal & Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision-making, achieve better 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® and Nexis® services.
About the RoleThis role supports Global Operations by designing, building, and maintaining reliable data pipelines and platforms. You will enable high‑quality analytics and AI‑ready data while improving automation, performance, and scalability. The role involves close collaboration with technical and business partners and encourages the responsible use of AI‑assisted engineering practices.
Key Responsibilities
+ Design, build, and maintain scalable data pipelines and integrations that support analytics and AI use cases.
+ Develop high‑quality, well‑tested code that follows best practices for readability, security, and maintainability.
+ Use SQL as a primary tool to query, transform, validate, and optimize data across systems.
+ Collaborate with technical and business partners to understand data needs and translate them into practical data solutions.
+ Document data requirements and contribute to specifications for analytics and AI‑enabled workflows.
+ Troubleshoot and resolve data issues through root cause analysis and appropriate automation or tooling.
+ Identify opportunities to improve efficiency through automation, orchestration, or AI‑assisted processes.
+ Participate in code reviews and development processes that promote reproducibility, transparency, and data quality.
+ Support database and data flow management to ensure systems meet organizational standards for reliability and AI readiness.
+ Stay current with evolving data engineering,