Senior Data Warehouse Engineer

About the project  

We are building a new Data Platform that consolidates data from various third-party SaaS products into centralized analytical environments such as Snowflake, Databricks, and Microsoft Fabric. The platform will provide clients with secure access to reporting and analytics-ready data through Data Warehouses and APIs. 
This is a greenfield initiative where the selected engineer will play a key role in defining architecture, selecting technologies, building ETL/ELT pipelines, and establishing the foundation for a long-term Data Platform offering. 

Requirements:  

• 5+ years of experience in Data Engineering or Data Architecture 
• Strong commercial experience with Snowflake and/or Databricks or Microsoft Fabric 
• Experience designing and building Data Warehouse or Data Lake solutions 
• Hands-on experience with Big Data processing 
• Strong ETL/ELT pipeline development experience 
• Excellent SQL skills 
• Experience working with large-scale datasets (terabytes of data) 
• Strong understanding of data modeling and performance optimization 
• Upper-Intermediate+ English 
• Ability to work independently and make architecture decisions 

 
Responsibilities: 

• Design and implement scalable Data Warehouse and Data Lake architectures 
• Build and optimize ETL/ELT pipelines 
• Develop data ingestion solutions for multiple third-party SaaS platforms 
• Process and transform large volumes of structured data 
• Design incremental data synchronization processes 
• Evaluate and introduce modern Data Engineering tools and technologies 
• Optimize platform performance, scalability, and cost efficiency 
• Collaborate with stakeholders to define the future architecture of the platform 
• Ensure high-quality, analytics-ready data for reporting and downstream consumer 

Schedule: Flexible Part-Time (approx. 20–25 hours per week). Agile schedule with the ability to balance daily workload (4–6 hours/day).No late meetings and overtime.   

Why Choose Us? What Do We Offer that's Exceptional?    

• Greenfield architecture with no legacy constraints 
• Opportunity to build a modern Data Platform from scratch 
• Freedom to influence technology choices and architectural decisions 
• Challenging Big Data and distributed data processing problems 
• High level of ownership and autonomy 
• Direct impact on a new strategic company initiative 
• Work with the latest technologies in the Data Engineering ecosystem