Data Analytics Engineer, Able

• Strong SQL experience with a deep understanding of analytical modeling concepts
• Hands-on experience with dbt (models, tests, docs, macros)
• Experience working 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 or implementing validation logic
• Ability to translate complex business processes into clean analytical datasets
• Strong documentation skills and attention to detail
• Experience collaborating with cross-functional teams and supporting analysts with reliable data
• Knowledge of Python is a plus (but not required)

Desirable

• We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization
• You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse)
• This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting
• You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered
• Design, build, and maintain dbt models (staging, core, marts) based on business needs
• Develop scalable SQL transformations and ensure efficient, cost-effective query performance
• Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability
• Create and maintain documentation for datasets, data lineage, business logic, and transformation rules
• Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness
• Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models
• Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting
• Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization
• Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making

• After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps:
• Recruiter Screening (40 minutes)
• Technical Interview (90 minutes)
• Final Interview (60 minutes)