Data Analytics Engineer
Indexed description
Company: Sonata Software
Team: Services, Data Team
About The Role
We are hiring a Data Analytics Engineer to scale how data is modeled, documented, and consumed across the organization. This role focuses on building clean, reliable, and business-ready datasets while owning the semantic layer and business metrics.
You will work closely with data engineering, analytics, and business stakeholders to ensure data is accessible, trusted, and usable for analytics.
Key Responsibilities
- Analytics Data Modeling (dbt + Snowflake)
- Build and maintain scalable data models using dbt on Snowflake
- Transform raw data into clean, analytics-ready datasets
- Design subject-area data models to simplify data consumption
- Ensure performance optimization and scalability of data models
- Semantic Layer & Metrics (Lightdash)
- Develop and manage semantic models and business metrics in Lightdash
- Define and standardize KPIs used across dashboards and reporting
- Ensure consistent business logic across all analytics use cases
- Maintain clear documentation and metric definitions
- Data Quality & Trust
- Implement data validation and testing frameworks within dbt
- Monitor and troubleshoot data quality issues
- Perform root cause analysis for discrepancies
- Collaborate with upstream data engineering teams to fix data issues
- Hands-on experience with:
- Snowflake (data warehousing)
- dbt (data transformation & modeling)
- Lightdash (semantic layer / BI tool)
- Strong SQL skills with experience handling large datasets
- Proven experience in building production-grade data models
- Solid understanding of data modeling concepts (dimensional modeling, star schema)
- Experience with orchestration tools (Airflow, etc.)
- Exposure to modern data stack environments
- Understanding of data governance and metadata management
- Experience working in client-facing or consulting environments
- 3–7 years of relevant experience in Analytics Engineering / Data Engineering / Data Analytics
- Demonstrated experience working in a modern data stack environment
- Strong ownership of data quality and reliability
- Ability to translate business requirements into scalable data models
- Detail-oriented with a focus on usability and clarity
- Strong collaboration and communication skills
Create a free Caio profile to unlock more results and save your role and location preferences.
Unlock free search