LexisNexis Risk Solutions
Data Engineer
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Are you passionate about building trusted customer data foundations that power meaningful marketing decisions?
Would you like to shape scalable data pipelines and models that improve customer journeys, analytics, and business outcomes?
About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, LexisNexis Risk Solutions | Transform Your Risk Decision Making
About our Team
This role acts as the technical backbone for the Marketing Hub — partnering closely with analytics, MarTech, and digital teams to deliver the pipelines, data models, and governance frameworks that support personalisation, measurement, experimentation, and strategic decision-making. The Data Engineer ensures data is not only accurate and compliant but designed for high performance and long-term growth.
About the Role
The Data Engineer plays a foundational role in enabling modern, insight-driven marketing by building and maintaining the data infrastructure that powers the organisation’s customer intelligence ecosystem. Working across the Customer Data Platform, journey orchestration capability, and advanced analytics and dashboarding environment, the role ensures that marketing has access to trusted, well-structured, and scalable data.
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Responsibilities
- Data Architecture & Pipeline Engineering: Build and maintain reliable pipelines integrating customer, campaign, and operational data into the CDP, ensuring accuracy and readiness for analysis
- Marketing Data Model Design: Develop scalable models supporting segmentation, lifecycle analytics, attribution, and KPI frameworks
- CDP Enablement & Identity Management: Partner with MarTech and Analytics to improve identity resolution, consent logic, and data quality within the CDP
- Journey Orchestration Data Readiness: Enable event streams, triggers, and monitoring to support accurate, timely customer journeys
- Analytics & Dashboarding Enablement: Provide structured datasets and semantic layers for reporting and dashboards, aligned with KPI frameworks
- Data Governance & Quality Assurance: Maintain data quality, lineage, and compliance with GDPR and PII standards
- Collaboration & Strategic Influence: Work closely with the Senior Marketing Analyst to align on priorities, translating technical concepts into business insights
- Operational Excellence & Standards: Ensure scalable, stable data operations using best practices in automation, observability, and continuous improvement
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Requirements
- Advanced SQL, ETL/ELT frameworks, and cloud data platforms such as Azure, AWS, or GCP
- Data modelling and pipeline orchestration using tools such as Airflow, Data Factory, dbt, or similar
- Integrate CDP data sources across digital, CRM, transactional, and behavioural data; support identity resolution, consent handling, audience activation, journey events, trigger logic, and behavioural signals
- Build data marts and semantic layers for dashboarding and BI tools such as Power BI or Tableau; structure datasets optimised for KPI frameworks, attribution, funnel, and lifecycle analytics
- Apply data quality frameworks, lineage, cataloguing, and GDPR/PII compliance practices
- Design secure data pipelines with monitoring, alerting, and SLA/SLO management
- Translate business and marketing needs into clear technical specifications, communicating technical topics clearly to non-technical stakeholders
- Partner with analytics, marketing ops, and MarTech teams to own scalable, reliable, trusted customer-data solutions
Risk benefit statement
Learn more about the LexisNexis Risk team and how we work here
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