Senior DWH Engineer Data Architect

About the project

We are building a new Data Platform thatconsolidatesdata from various third-party SaaS products into centralized analytical environments such asSnowflake Databricks and Microsoft 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 andestablishingthe foundation for a long-term Data Platform offering.

Requirements:

• 5 years of experience in Data Engineering or Data Architecture

• Strong commercial experience withSnowflakeand/orDatabricksorMicrosoft 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 andoptimizeETL/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

• Optimizeplatform 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. 2025 hours per week). Agile schedule with the ability to balance daily workload (46 hours/day).Nolatemeetingsandovertime.

Why Choose Us What Do We OfferthatsExceptional

• 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 levelof ownership and autonomy

• Direct impact on a new strategic company initiative

• Work with the latest technologies in the Data Engineering ecosystem