Data Engineer III
Join a Collaborative, Inclusive Team Driving Innovation in Healthcare Technology
Make a Difference with Us
Are you passionate about developing high-quality software that positively impacts people’s lives?
Do you enjoy collaborating with others and inspiring those around you?
If you’re looking for a place where your skills, perspectives, and ideas are valued, we encourage you to consider this opportunity.
About the Business
At LexisNexis Risk Solutions, we help organizations assess and manage risk, with a special focus on advancing health outcomes and improving healthcare operations. We are committed to privacy, security, accessibility, and the ethical use of data. Our diverse teams leverage robust data, innovative technology, and applied analytics to make a positive difference. To learn more, please visit https://risk.lexisnexis.com/healthcare.
About the Team
We organize our teams into collaborative Squads, each bringing together people from many backgrounds and with a variety of skills and perspectives. This structure encourages open communication, shared learning, and the opportunity for everyone to contribute toward our goals. We value each team member’s unique experience and input.
About the Role
This role performs moderate-complexity research, design, and data engineering assignments within a defined engineering functional area or product line, with emphasis on Python development basics, workflow automation, AI-enabled process improvement, and production support readiness.
Responsibilities
Own and manage assigned data transfers, including collection setup, transfer setup, contributor onboarding, and customer onboarding activities.
Partner with technical stakeholders to gather, analyze, document, and finalize business and technical requirements for data engineering solutions.
Design, develop, maintain, and support efficient, reusable, and high-quality Python-based data pipelines, workflows, and automation solutions.
Write and review technical specifications, oversee dataflow and database structures, and ensure compliance with engineering standards and best practices.
Troubleshoot production and data engineering issues, perform root cause analysis, implement bug fixes, and drive permanent corrective actions.
Monitor pipeline health, data quality, SLA adherence, and operational performance while providing L2/L3 production support, incident management, and service recovery.
Collaborate with cross-functional teams to translate product requirements into scalable data engineering solutions and continuously improve processes through automation and AI-driven approaches.
Participate in code reviews, project reviews, operational documentation, knowledge development, and ongoing learning to deliver high-quality services aligned with changing business priorities.
Requirement
4+ years of experience in Data Engineering or Data Management with demonstrated technical competency and experience supporting production data pipelines and systems.
Bachelor’s degree in Engineering, Computer Science, or a related field, or equivalent practical experience.
Strong proficiency in Python for data processing, scripting, pipeline development, automation, operational tooling, and production support activities.
Proficiency in SQL and data manipulation/query languages, with solid understanding of file management and data transfer processes.
Experience with automation frameworks and tools to improve reliability, operational efficiency, scalability, and service quality.
Working knowledge of AI-assisted techniques, software engineering best practices, Agile methodologies, and development environments including Windows, Unix, and HPCC.
Strong analytical, problem-solving, communication, collaboration, and attention-to-detail skills, with the ability to learn new tools, technologies, and processes.
Hands-on experience in production support, including monitoring, alert triage, log analysis, incident management, stakeholder communication, service recovery, and operational documentation.
Learn more about the LexisNexis Risk team and how we work
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