Senior Data Engineer

About the role

NHG is looking for a Senior Data Engineer to design, develop, implement and support scalable enterprise data solutions that deliver reliable, secure and high-quality data. The role builds and maintains data pipelines, transformations, data models and data platforms using Databricks, SQL and Informatica Intelligent Data Management Cloud (IDMC), working with business stakeholders, project teams, infrastructure teams and vendors.

Key responsibilities

• Design, develop and maintain scalable data pipelines, ETL/ELT processes, ingestion frameworks and transformation solutions using Databricks, SQL and IDMC.
• Build and maintain data models, fact and dimension tables, Delta tables, reusable data assets and enterprise data structures.
• Ensure data quality through validation, reconciliation, integrity checks, source-to-target verification and automated quality controls, and resolve data discrepancies.
• Provide production support, troubleshoot pipeline failures, perform root cause analysis, implement permanent fixes and maintain runbooks.
• Maintain compliance with standards, security, coding practices, testing, deployment and governance, and take part in peer reviews.
• Work with business analysts, data analysts, project managers, platform teams and vendors to turn requirements into technical solutions, and drive automation, performance optimisation and platform modernisation.

Required profile

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Information Systems or a related field.
• At least 8 years of experience in Data Engineering, ETL/ELT, Data Integration or Data Platform implementation, with proven enterprise-scale delivery.
• Experience with multiple source systems, complex data ecosystems and production data environments.
• Candidates with fewer years of experience may be considered for a Data Engineer role.

Required skills

• Strong hands-on SQL and batch and incremental pipelines in Databricks and IDMC.
• Understanding of ETL/ELT, data integration, data warehousing, data lakes and lakehouse architectures.
• Data modelling (relational and dimensional), data quality management, troubleshooting and root cause analysis.
• Production support and DataOps, including CI/CD.
• Preferred: enterprise data platform design with Databricks/IDMC, streaming pipelines, Unity Catalog, access management, data governance, performance tuning and cost optimisation.