Data Architect / Senior Data Engineer

Webellian is a Digital Transformation and IT consulting company serving sectors including insurance, banking, healthcare, retail, and manufacturing. Its team includes engineers and senior advisors working across industries to accelerate client vision and strategy.

• Proven senior-level experience in Data Engineering, Data Architecture, or similar roles.
• Strong hands-on experience with Databricks.
• Advanced knowledge of Python and practical experience with PySpark.
• Strong experience designing and optimizing ETL and ELT pipelines.
• Very good knowledge of SQL and relational databases.
• Experience in data architecture, data modelling, and scalable data platform design.
• Practical experience with dbt.
• Experience with Microsoft Azure or other cloud-based data platforms.
• Knowledge of CI/CD practices and tools, particularly GitHub.
• Understanding of cloud infrastructure, orchestration, deployment, and data security.
• Ability to translate complex requirements into scalable technical solutions.
• Experience working in large, distributed, or multi-team environments.
• Fluent English (C1) and strong communication skills.
• Proactive attitude, ownership, and a DevOps mindset.

Nice to have:

• Experience with Oracle databases.
• Experience with dltHub.
• Experience with Azure Data Services.
• Experience supporting Power BI reporting environments.
• Knowledge of data governance, lineage, and metadata management.
• Experience building enterprise-level data platforms or data lakes.
• Industry experience in insurance or financial services.
• Bachelor's or Master's degree in a technical field.

• Design and develop the architecture of a large-scale Central Data Platform.
• Build and maintain end-to-end data pipelines for ingestion, transformation, storage, and consumption.
• Integrate data from multiple systems into a consistent data platform.
• Design scalable data models aligned with business and analytical requirements.
• Develop and optimize ETL and ELT processes using Databricks, Python, PySpark, and dbt.
• Prepare reliable datasets for analytics, reporting, and business intelligence.
• Ensure data quality, consistency, lineage, security, and performance.
• Develop reusable data components and standardize data workflows.
• Support automated deployment and infrastructure processes using GitHub and Terraform.
• Collaborate with data engineers, architects, analysts, Power BI developers, and business stakeholders.
• Translate complex requirements into scalable technical solutions.
• Continuously improve the platform's reliability, automation, and maintainability.

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