About this role
Role Description
Join a forward-thinking consulting environment that helps companies scale through modern cloud, data, and AI-driven solutions. The team partners with organizations across industries to design and implement robust, multi-cloud architectures, combining deep infrastructure expertise with advanced data platforms.
Design and implement data transformation pipelines and semantic data models using dbt or equivalent frameworks. Build and maintain data marts, aggregated layers, and consumption-ready datasets aligned to project and stakeholder requirements. Develop dashboards, reports, and self-service analytics environments using modern BI tooling (Power BI, Looker, Tableau, or equivalent). Define and implement a semantic layer that bridges technical data models and business-friendly metrics. Collaborate with the Senior Analytics & Data Engineer on transformation layer design and integration patterns. Document data lineage, business metric definitions, data dictionaries, and dashboard logic consistently. Implement data validation checks within transformation pipelines and work with the DataOps Engineer on quality automation. Participate in agile ceremonies across Scrum and Kanban workstreams. Use AI-assisted tools to accelerate model development, documentation, dashboard scaffolding, and query optimization.
Qualifications
4+ years in analytics engineering, BI development, or data engineering roles spanning both transformation and visualization layers. Strong SQL proficiency and hands-on experience with cloud data warehouses (Snowflake, BigQuery, Redshift, Synapse, or equivalent). Hands-on dbt experience (Core or Cloud) or equivalent transformation framework. Proficiency with at least one major BI tool - Power BI, Looker, Tableau, or equivalent - including semantic/data model configuration. Understanding of dimensional modeling and semantic layer design. Ability to translate business requirements into data models and visualizations without heavy hand-holding. Experience working in agile delivery environments. Strong written English for documentation, metric definitions, and async communication.
Requirements
Experience with headless BI or semantic layer tools (Cube, Metriql, dbt Semantic Layer). Python skills for custom transformation logic or data pipeline scripting. Exposure to multiple cloud platforms. Experience designing self-service analytics environments for non-technical end users. Multi-industry background - analytics patterns vary significantly by vertical.
Benefits
People-first management with minimal bureaucracy. A friendly company culture, proven by employees who choose to return. Flexible working hours. Full financial and legal support for independent contractors. Free English classes, with native speakers or Ukrainian teachers. Dedicated HR support.