Data Analytics Engineer

Ruby Labs is a leading tech company that creates and operates innovative consumer products. They work across the health, education, and entertainment industries, driving the future of consumer-led products.

• Strong SQL experience with deep understanding of analytical modeling concepts
• Hands-on experience with dbt including models, tests, docs, and macros
• Experience with cloud data warehouses such as BigQuery or ClickHouse
• Understanding of data modeling patterns
• Familiarity with data engineering concepts: ELT/ETL, orchestration, version control, CI/CD
• Experience with data quality frameworks and validation logic
• Ability to translate complex business processes into analytical datasets
• Strong documentation skills and attention to detail
• Experience collaborating with cross-functional teams supporting analysts with reliable data
• Knowledge of Python is a plus (not required)

Nice To Haves:

• Experience with orchestration tools like Airflow, Dagster
• Experience with marketing, product, or billing datasets
• Exposure to metric layers, semantic layer design, or BI governance
• Basic understanding of observability tooling such as Grafana, Prometheus
• Experience optimizing BigQuery costs or ClickHouse performance

• Design, build, and maintain dbt models (staging, core, marts) based on business needs
• Develop scalable SQL transformations ensuring efficient query performance
• Implement data quality tests including dbt tests, custom checks, and anomaly detection
• Create and maintain documentation for datasets, lineage, business logic, and transformation rules
• Collaborate with Data Engineers to improve raw data structures and ingestion patterns
• Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into reusable data models
• Monitor data pipelines and marts, resolve issues, and prevent inconsistencies
• Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization
• Contribute to continuous improvement of data modeling frameworks, performance tuning, and technical decision-making

Applicants must be located within approximately ± 4 hours of the CET (Central European Time) zone to ensure optimal collaboration.