Senior Data Engineer (Azure)

Role Summary

We are looking for a Senior Data Engineer to support the Data & Analytics function of a leading global insurance company.

The role is responsible for designing, developing, and maintaining scalable data pipelines and data solutions within a Microsoft Azure and Databricks environment. The successful candidate will work closely with data, analytics, AI, and business teams to integrate data from multiple sources and make it available for analytics, reporting, and AI use cases.

The ideal candidate has strong hands-on experience with Databricks, Azure, SQL, Python/PySpark, and modern cloud-based data engineering practices, with the ability to independently deliver reliable and maintainable data solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and data processing workflows using Azure and Databricks
• Build data ingestion, transformation, and integration processes across multiple data sources
• Develop and maintain ETL/ELT pipelines supporting analytics, reporting, and AI use cases
• Develop data processing solutions using SQL, Python, and PySpark
• Design and optimize datasets and data models for efficient downstream consumption
• Perform data validation and implement data quality checks to ensure accuracy and reliability
• Monitor, troubleshoot, and optimize data pipelines and processing performance
• Collaborate with Data Analysts, AI Engineers, architects, and business stakeholders to understand data requirements
• Support deployment and productionization of data solutions
• Maintain clear technical documentation covering data flows, transformations, dependencies, and business rules
• Follow appropriate engineering, security, data governance, and development standards

Required Skills & Experience

• At least 5 years of experience in Data Engineering, Data Platform Engineering, or a similar role
• Strong hands-on experience with Microsoft Azure and Databricks
• Strong SQL skills, including complex queries, data transformations, and query optimization
• Strong practical experience with Python and/or PySpark
• Proven experience designing and implementing ETL/ELT pipelines
• Experience working with large datasets and integrating data from multiple sources
• Good understanding of data modelling, data quality, and data integration principles
• Experience working with cloud-based data platforms and modern data architectures
• Good understanding of software engineering practices, version control, testing, and CI/CD
• Strong analytical and problem-solving skills
• Ability to work independently and collaborate effectively with both technical and business stakeholders

Preferred Qualifications

• Experience within insurance, financial services, or another regulated industry
• Knowledge of additional Azure data services and cloud data architecture
• Experience working with data lakes, lakehouse architectures, and enterprise data platforms
• Familiarity with data governance, metadata, lineage, and data security principles
• Exposure to Databricks Genie and AI-enabled data and analytics capabilities
• Experience supporting data platforms used for AI/ML or Generative AI workloads
• Experience working in Agile delivery environments

Key Behavioral Expectations

• Strong ownership and ability to deliver with limited supervision
• High attention to data quality, reliability, and maintainability
• Strong analytical and troubleshooting mindset
• Clear communication with both technical and non-technical stakeholders
• Pragmatic, collaborative, and delivery-focused approach
• Ability to operate effectively within a complex enterprise environment

(Note: Due to the high volume of applications we receive, we are unable to respond to every candidate individually. If you have not received a response from GFT regarding your application within 10 workdays, please consider that we have decided to proceed with other candidates. We truly appreciate your interest in GFT and thank you for your understanding)