Data Engineering Specialist
Pubblicato il 23-08-2026 - Africa Specialty Risks in Veneto
Role Purpose
As a Data Engineer Specialist, you will play a pivotal role in supporting the development, maintenance, and optimisation of the organisation’s data infrastructure. The role would focus on managing data pipelines, transforming raw data into usable structures, automating ingestion processes, and enabling high-quality reporting and analytics across the business. This role plays a key part in supporting cloud-based reporting, enterprise data warehouse initiatives, and cross-departmental analytics.
Role Responsibilities
Data Pipeline & ETL Development
- Construct, test, and maintain scalable data pipelines.
- Building ETL/ELT processes for ingesting and transforming large datasets.
- Automate data ingestion using tools such as Python, SQL, cloud ETL platforms
Data Architecture & Storage
- Develop and maintain database schemas, views, mapping tables, data models, and data warehouses.
- Optimise data storage solutions (SQL, NoSQL, cloud storage, data lakes).
Data Integration & APIs
- Integrate internal and external data sources.
- Build APIs and services for data access and consumption.
- Monitor system interoperability and consistent data flow across platforms.
Data Quality, Validation & Governance
- Automate validation rules, quality checks, and data cleaning procedures.
- Monitor data accuracy, data consistency,
data security, and data regulatory compliance.
Infrastructure Management
- Assist the configuration and management of databases, warehouses, cloud computing frameworks.
Collaboration & Support
- Work closely with data scientists, analysts, product teams, and executives.
- Assist Pricing & Exposure management with their respective databases
- Translate complex technical concepts into actionable insights for non-technical stakeholders.
- Support organizational decision-making by ensuring data is accurate, accessible, and relevant.
Skills & Experience
Essential
- Hands on experience with Python and SQL.
- Exposure with cloud platforms (AWS, Azure, GCP).
- Experience with ETL tools: Databricks, Azure Data Factory, SSIS.
- Strong understanding of data modelling, metadata, and data architecture.
- Ability to build high-performance algorithms and prototypes.
- Knowledge of data governance, data security, and privacy standards.
- Excellent problem-solving and analytical thinking.
- Ability to collaborate cross‑
- Clear communication and documentation abilities.
Desirable
- Understanding of machine-learning data requirements.
- Experience working within cross functional analytical squads.
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