Would you like to be part of a team that delivers high-quality software to our customers?
Are you a visible champion with a ‘can do’ attitude and enthusiasm that inspires others?
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,
https://risk.lexisnexis.com
About the Team
Team is tech hub for rest of the organization and hence working across multiple domains – US Healthcare, Insurance, Government etc. Team primarily works on data extraction, manipulation, loading, and analytics using technologies like Python,
PySpark
, Bigdata – HPCC, language ECL, Talend, Azure tools (Synapse, ADF
etc
), Power BI, Essbase
etc
About the Role
W
e are looking for a
Data Engineer
to design, build, and maintain data pipelines and analytics-ready datasets on Azure-based platforms. You will collaborate with cross-functional teams to deliver high-quality, secure, and scalable data solutions that enable informed decision-making.
Responsibilities:
Design and maintain reliable data pipelines using Azure and big data technologies.
Ingest and transform data from multiple sources such as databases, APIs, and files.
Build and optimize data processing workflows using PySpark, Spark SQL, and Databricks.
Develop and manage analytics
‑
ready datasets to support reporting and insights.
Ensure data quality, consistency, security, and governance across data platforms.
Monitor, troubleshoot, and improve data pipelines for performance and scalability.
Collaborate with business, analytics, and engineering teams to understand data needs.
Document data solutions and follow best practices through code reviews and standards.
Requirements:
Around 4+
years of experience
in data engineering or related roles.
Strong knowledge of
SQL
and data transformation concepts.
Hands-on experience with
Azure Databricks,
PySpark
, and Spark SQL, Python
Experience with
Azure Data Factory, Azure Data Lake Storage (ADLS Gen2), and Azure SQL or Synapse
AI tools, preferred-
github
copilot, M365, Visual studio core
Understanding of
data warehousing concepts and ETL/ELT patterns.
Experience working with large, structured, or semi-structured datasets.
Familiarity with Agile ways of working.
Ability to communicate ideas clearly and work collaboratively across teams.
Learn more about the LexisNexis Risk team and how we work



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