Data Analytics Engineer

As a Data Analytics Engineer, you’ll turn raw data into reliable datasets that teams can use to make decisions. You’ll work with data engineers, analysts, and business stakeholders to define metrics, build reusable data models, and make data easier to find, understand, and use.

You’ll own the analytics transformation layer, from gathering requirements and designing models through testing, deployment, and ongoing maintenance.

• →Design, build, and maintain SQL-based data models in the cloud data warehouse to support reporting, self-service analytics, and business analysis.
• →Translate business requirements into reusable datasets and work with stakeholders to define consistent KPIs across reports and dashboards.
• →Build and maintain automated data tests and monitoring. Investigate data quality issues, trace them to their source, and coordinate fixes with the teams responsible.
• →Document data models, metric definitions, and data lineage so users can understand where data comes from and how to use it.
• →Improve query and transformation performance while managing warehouse compute costs.
• →Apply software engineering practices to analytics workflows, including version control, code reviews, and automated deployments.

• Professional experience in analytics engineering, data engineering, BI engineering, or a related role, with hands-on ownership of production data models or pipelines.
• Advanced SQL skills, including complex joins, window functions, and query optimization.
• Experience designing analytical data models, including defining table grain and working with facts and dimensions.
• Hands-on experience with a cloud data warehouse such as Snowflake, BigQuery, Redshift, or Databricks SQL, and a transformation framework such as dbt or an equivalent.
• Experience with Git, code reviews, automated data testing, and deployment workflows.
• Ability to translate business questions into data requirements and communicate modeling decisions to technical and non-technical stakeholders.
• Professional proficiency in English, both written and spoken.

• Experience with Python for data processing or automation, or with orchestration tools such as Airflow or Dagster.
• Experience with BI tools such as Looker, Power BI, or Tableau, or with a semantic layer for managing shared metrics.
• Familiarity with data observability, data catalogs, or governance practices such as lineage tracking and access management.