Senior Analytics Engineer - Databricks, Semantic Layer & AI Readiness

Senior Analytics Engineer - Databricks, Semantic Layer & AI Readiness Be a part of building the future of crypto trading analytics and AI-powered data intelligence at the forefront of technology! Trading since 2017, Gravity Team is one of the leading crypto market makers and liquidity providers, with cumulative trading volumes to date in excess of $400 billion. We provide 24/7 liquidity across 1,400+ crypto-asset pairs on 30+ exchanges in 15+ countries, representing roughly 1% of global spot trading volume. We’re looking for a Data Analytics Engineer to help build and scale our next-generation analytics platform in Databricks, powering trusted data products, real-time insights, self-service analytics, and the future of AI-driven decision-making. What You’ll Do: Databricks ingestion and warehouse execution Gold models, marts, and semantic layer delivery Genie and AI-readiness of the data layer Data quality, monitoring, and validation Stakeholder enablement and warehouse adoption Financial and business reporting support Team collaboration and ways of working Work closely with the BI Lead to turn roadmap items into concrete technical deliverables. Provide effort estimates, implementation tradeoffs, and delivery risks. Participate in code review, documentation, and shared standards for SQL, notebooks, jobs, and data assets. Help strengthen self-service analytics by creating reusable assets and reducing manual workflows. Qualifications: Strong fit Strong SQL skills and solid Python skills. Hands-on experience with Databricks, Spark, Delta Lake, or a similar modern cloud data platform. Experience building and maintaining ELT/ETL pipelines, especially with incremental or CDC-based patterns. Experience with dimensional modeling, marts, semantic layers, and warehouse-first business logic. Experience supporting self-service analytics or BI platforms used by multiple teams. Ability to work directly with stakeholders and translate messy business requirements into clean data products. Strong debugging, validation, and documentation habits. Good to have Experience with Power BI, Grafana, dbt, Airflow, Kafka, or similar tooling. Familiarity with Unity Catalog, fine-grained access patterns, or governed data products. Exposure to natural-language BI, semantic metadata, or preparing datasets for AI-facing analytics tools. Experience in financial, trading, or crypto-adjacent environments. Familiarity with GitLab, Jira, notebooks, and collaborative analytics development workflows. Department Data Science Locations Latvia (Riga) Remote status Fully Remote Employment type Full-time