Senior Software Engineer II

Would you like to be part of a team that delivers high-quality software to our customers?

Are you passionate about technology and innovation?

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. Learn more about LexisNexis Risk at risk.lexisnexis.com.

About the Team:

• Team is tech hub for rest of the organization and hence working across multiple domains – 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 (Data Synapse, ADF etc), Power BI, etc.

About the Role

We are seeking a dedicated and skilled developer to join our team of Data engineers and Data Analysts working with various stakeholders.

As a Senior Azure Data Engineer,

• You will be responsible to provide for designing, implementing, and maintaining sophisticated software solutions, leveraging your expertise in Python, PySpark, SQL, and API integration on Azure platform services like ADF and Databricks.

• You will also leverage various AI Agents like Genie and Copilot to improve productivity.

Responsibilities:

• Data Pipeline Engineering:
  Design and build scalable, high-performance ETL/ELT pipelines using Azure Data Factory and Azure Databricks, handling ingestion and transformation of data from diverse internal and external sources.

• Advanced Data Preparation & Transformation:
  Implement complex transformation logic using PySpark and SQL to standardize, cleanse, and normalize large-scale datasets including nested JSON.

• Data Modeling:
  Create and maintain dimensional models (star/snowflake schemas) and structured outputs to support analytics, reporting, and regulatory use cases.

• Performance Optimization:
  Profile and tune PySpark jobs for performance using techniques such as caching, partitioning, and broadcast joins. Ensure pipelines scale efficiently with increasing data volumes.

• Governance & Quality Assurance:
  Apply robust data validation, quality checks, and maintain metadata lineage with Unity Catalog. Adhere to governance standards across the pipeline lifecycle.

• Cross-Functional Collaboration:
  Partner with business analysts, product managers, data scientists, and QA teams to understand data requirements and deliver fit-for-purpose solutions.

• Documentation & Knowledge Sharing:
  Maintain comprehensive documentation on data flows, architecture, and models. Support upskilling and mentoring of junior team members.

• AI-Enhanced Development:
  Utilize Databricks Genie and related AI assistants to accelerate development, ensure code quality, and assist in troubleshooting.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or equivalent practical experience.

• Total experience of 6 to 8 years and 5+ years in data engineering roles with hands-on experience in PySpark, SQL, and Azure-based data ecosystems.

• Strong background in building robust and maintainable pipelines using Azure Data Factory and Databricks.

• Proven expertise in data modeling, schema design, and large-scale data preparation for analytics.

• Proficient in Git, version control workflows, and CI/CD practices.

• Solid understanding of data governance, metadata management, and data quality frameworks.

• Exposure to Unity Catalog, Databricks Genie, or other AI-enhanced data engineering tools is preferred.

• Excellent analytical, debugging, and stakeholder communication skills. Familiarity with Agile or Scrum-based delivery.

• Experience with machine learning workflows and integrating model outputs into data pipelines.

• Exposure to Salesforce, Alfresco, or Schedule A contract data formats is a bonus.

Learn more about the LexisNexis Risk team and how we work at here

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