Data Engineer - Insurance, Regional, 12 months
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Summary
Data Engineer for a leading insurance group, responsible for designing and maintaining data pipelines, ETL/ELT processes, and MI dashboards using Python, SQL, and cloud platforms like Databricks or AWS.
As a Data Engineer within the insurance industry, you will play a crucial role in managing and optimising data systems and processes. This position involves leveraging your technical skills to support the organisation's technology initiatives effectively.
Leading insurance group
Description
As a Data Engineer, your main responsibilities will include:
- Design, develop, and maintain data pipelines to support reporting, analytics, and operational requirements.
- Assist in data modelling, schema design, and database development to ensure data quality and scalability.
- Perform data extraction, transformation, and loading (ETL/ELT) activities from multiple data sources.
- Develop management information (MI) reports, dashboards, and visualizations for business stakeholders.
- Support cloud-based data platforms and contribute to data migration and transformation initiatives.
- Collaborate with business users, analysts, and technology teams to understand and deliver data requirements.
- Assist in data governance, data quality monitoring, and documentation activities.
- Participate in AI/ML-related initiatives and proof-of-concepts where applicable.
Profile
A successful Data Engineer should have:
- Fresh graduates with strong academic performance and relevant internship/project experience are welcome.
- 1-2 years of experience in data engineering, data analytics, business intelligence, or related fields is preferred.
- Experience within banking, insurance, financial services, or other regulated industries is advantageous but not mandatory.
- Hands‑on programming experience in Python, SQL, or other relevant programming languages.
- Experience with data visualization and dashboard development using tools such as Power BI, Tableau, Qlik Sense, Looker Studio (Data Studio), or similar platforms.
- Exposure to cloud-based data platforms such as Databricks, AWS, Azure, Google Cloud Platform (GCP), or equivalent technologies.
- Understanding of database concepts, data modelling, ETL/ELT processes, and data warehousing principles.
- Knowledge or experience in Machine Learning (ML) and Artificial Intelligence (AI) projects would be an advantage.