Senior Analytics Engineer
About the Role
You will design, build, and maintain reliable data models and transformation pipelines that turn raw data into business-ready datasets. You will define and implement metrics and semantic layers, write efficient SQL and dbt transformations, ensure data quality and governance, and create dashboards and reports to enable data-driven decisions.
Requirements
- Over 6 years of experience as a Analytics Engineer
- Previous experience developing and tracking KPIs for public companies
- Expert-level SQL skills with experience writing complex queries and optimizing performance
- Hands-on experience with dbt for data transformation and modeling
- Strong understanding of data modeling concepts such as star schema snowflake schema and dimensional modeling
- Familiarity with BI and dashboarding tools such as Looker Superset Tableau Power BI
- Experience defining KPIs and metrics for business stakeholders
- Comfort with Python or other scripting languages for lightweight data transformations and automation
- Knowledge of data governance lineage and documentation tools such as DataHub Great Expectations
- Understanding of cloud data warehouses such as Snowflake BigQuery Redshift
- Experience with version control and CI/CD practices in analytics workflows such as GitHub Actions
Responsibilities
- Design build and maintain reliable data models that transform raw data into business-ready datasets
- Collaborate with analysts data scientists and business stakeholders to translate requirements into actionable metrics and KPIs
- Develop and maintain metrics definitions semantic layers and data documentation to ensure consistency
- Build optimize and test dbt models to deliver clean reliable and trusted data
- Ensure data quality accuracy and governance are embedded in models and pipelines
- Create dashboards reports and visualizations that empower business users to make data-driven decisions
- Write efficient SQL queries and maintain performant models
- Partner with data engineers to ensure smooth data ingestion and availability for analytics
- Continuously improve processes and workflows to increase efficiency reliability and scalability