Senior DWH Engineer / Data Architect

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 meetingsand 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 Originally posted on Himalayas