Are you an experienced Data Engineer with a ‘can do’ attitude and enthusiasm that inspires others?  

Do you enjoy being part of a team that works with a diverse range of technology?  

About our Team  

At LexisNexis Intellectual Property Solutions, our mission is to bring clarity to innovation by delivering better outcomes to the innovation community. Each and every day, the work our team does supports the development of new technologies and processes that ultimately advance humanity. Helping our customers reach their goals is our primary focus. We enable innovators to accomplish more by helping them make informed decisions, be more productive, comply with regulations and ultimately achieve superior results. Our overall success is measured by how well we deliver these results. We are proud to directly support and serve these innovators in their endeavors to better humankind.

  
About the Role     

As a  Data Engineer at LexisNexis Intellectual Property (LNIP), you will design, build, and maintain complex data systems that support both traditional analytics and emerging AI/ML-driven capabilities. You’ll contribute to the evolution of a modern Data Platform that underpins mission-critical products, including data pipelines that enable machine learning, LLM-powered features, and AI experimentation at scale. Your role will be critical in ensuring the integrity, security, observability, and accessibility of business-critical data. 

Responsibilities:

• Be a hands-on Data Engineer working across our ecosystem of products and Data Platforms 

• Interface with data scientists, AI engineers, and other technical stakeholders to finalize requirements and enable production-ready AI use cases. 

• Develop and maintain data infrastructure supporting real-time and batch data processing in streaming and event-driven architectures. 

• Build and operate scalable data ingestion, feature engineering, and ML/LLM data pipelines, incorporating Data Lakehouse concepts for unified data management. 

• Support MLOps workflows, including dataset versioning, experiment tracking inputs, model training data pipelines, and inference data flows. 

• Successfully implement development processes, coding best practices, automated testing, and code reviews. 

• Design and maintain APIs and data services that support analytics, ML, and downstream AI applications, ensuring strong data lineage and observability. 

• Apply DataOps principles to improve system reliability, performance, monitoring, and deployment automation. 

• Automate system lifecycle management and infrastructure where appropriate to ensure robustness, scalability, and cost efficiency. 

• Collaborate on the integration of LLMs and AI services (e.g., embedding pipelines, vectorized data preparation, prompt-related data flows). 

• Resolve technical issues as necessary and contribute to root cause analysis. 

Requirements: 

• Strong experience with SQL Server and cloud-based Data Lakes (Azure and/or AWS). 

• Proven background in modern Data Engineering, including building production‑grade pipelines. 

• Deep knowledge of large-scale data platforms such as Databricks and Snowflake. 

• Familiarity with cloud-native tools including Azure Synapse and Redshift. 

• Experience building data pipelines that support AI/ML workloads. 

• Understanding of Scrum, Kanban, and Agile software development methodologies. 

• Hands‑on experience with Spark. 

• It would also be advantageous to have familiarity with technologies like Elasticsearch, Solr, PostgreSQL, Databricks, Delta Lake, and Delta Share.