Data Engineer/Consultant (Senior/Staff)

VirtusLab is a technology company building a small (4-6 people), agile, cross-functional team to deliver a complete data platform for a fast-scaling client in the Insurance sector. The project involves architecting a modern Data Platform consolidating fragmented legacy systems, organizing data from numerous sources, and establishing a standardized, governed, and future-proof data foundation to enable faster decision-making and AI readiness.

• Proactive approach and flexibility in action.
• Very good command of English (written and spoken).
• Hands-on experience with Python.
• Proven experience with data warehouse solutions such as BigQuery, Redshift, Snowflake.
• Experience with Databricks or data lakehouse platforms.
• Strong background in data modelling, data catalogue concepts, data formats, and data pipelines/ETL design, implementation, and maintenance.
• Ability to thrive in an Agile environment and collaborate transparently.
• Experience with AWS, GCP, or Azure cloud services including GCS/S3/ABS, EMR/Dataproc, MWAA/Composer or Microsoft Fabric, ADF/AWS Glue.
• Experience in ecosystems requiring improvements and drive to implement best practices.
• Experience with Infrastructure as Code practices, particularly Terraform, is an advantage.

• Collaborate closely with client specialists and stakeholders to define requirements, develop data pipelines, and data quality metrics.
• Participate in defining requirements and architecture for the new platform, implement the solution, and remain involved in its operations and maintenance post-launch.
• Introduce data governance and management to enable accurate and comprehensive reporting.
• Build data ingestion and processing pipelines focused on customer needs.
• Establish a production-ready, fully operational Snowflake environment and drive operational excellence.
• Translate complex business logic into accurate data models reflecting business reality.
• Integrate diverse data sources to build reliable data products and comprehensive data dictionaries.
• Manage the full Data Engineering and Data Science lifecycle to support production ML and AI experimentation.
• Take ownership from concept to deployment.
• Promote automation, CI/CD, and rigorous engineering standards.