Data Analytics Engineer

avacone Switzerland; Basel; On-site; CHF 110,000 - 150,000; Full time

Job summary

avacone Switzerland is seeking a Data Analytics Engineer in Basel to turn raw data into reliable datasets that teams can use to make decisions. You will own the analytics transformation layer from gathering requirements and designing models through testing, deployment, and ongoing maintenance.

You will design SQL-based data models in a cloud data warehouse (Snowflake, BigQuery, Redshift, or Databricks SQL), translate business questions into reusable datasets, and define KPIs across reports.

Qualifications

• Hands-on ownership of production data models or pipelines.
• Advanced SQL skills with complex joins and window functions.
• Experience with a cloud data warehouse (Snowflake/BigQuery/Redshift/Databricks).
• Experience with Git, code reviews, and deployment workflows.
• Proficiency in English (written and spoken).

Responsibilities

• Design, build, and maintain SQL-based data models in the cloud data warehouse to support reporting and analytics.
• Translate business requirements into reusable datasets and define consistent KPIs across reports and dashboards.
• Build and maintain automated data tests and monitoring; investigate data quality issues.
• Document data models, metric definitions, and data lineage so users can understand where data comes from and how to use it.
• Improve query performance while managing warehouse compute costs.
• Apply software engineering practices to analytics workflows, including version control and automated deployments.

Skills

Advanced SQL

Stakeholder communication

English proficiency

Analytical thinking

Tools

Snowflake

BigQuery

Redshift

Databricks SQL

dbt

Git

Airflow

Dagster

Job description

About The Role

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.

Key Responsibilities

• 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.

Required qualifications

• 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.

Preferred qualifications

• 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.