Data Analytics Engineer

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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 (Big Query, Click House). 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

• 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 Big Query or Click House
• 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)

• Experience with orchestration tools (Airflow, Dagster)
• Experience working with marketing, product, or billing datasets
• Exposure to metric layers, semantic layer design, or BI governance
• Basic understanding of observability tooling (Grafana, Prometheus, ... )
• Experience optimizing Big Query costs or Click Ho