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
