Data Analytics Engineer

avacone Switzerland

. Design, build, and maintain SQL-based data models in the cloud data warehouse for reporting, self-service analytics, and business analysis.

Posted 11/6/2026full-timeBasel • SwitzerlandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in SQL-based data modeling and analytics engineering, with a strong focus on cloud data warehouse solutions and automated data testing. Capable of translating business requirements into actionable data insights while ensuring data quality and performance optimization.

Highest-signal resume keywords

Advanced SQL SkillsCloud Data Warehouse ExperienceData Model DesignAutomated Data TestingBusiness Requirement Translation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLData ModelingQuery OptimizationData TransformationData Quality InvestigationVersion ControlAutomated DeploymentsData Lineage DocumentationKPI DefinitionData Pipeline Ownership

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

SnowflakeBigQueryRedshiftDatabricks SQLDbtGitLookerPower BITableauAirflow

Industry Keywords

Analytics EngineeringData EngineeringBI EngineeringData GovernanceData Observability

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudPythonSnowflakeSQLTableau

About the role

Key responsibilities & impact

Design, build, and maintain SQL-based data models in the cloud data warehouse for reporting, self-service analytics, and business analysis.
Translate business requirements into reusable datasets and define consistent KPIs with stakeholders 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 responsible teams.
Document data models, metric definitions, and data lineage.
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.
Own the analytics transformation layer from requirements gathering and model design through testing, deployment, and ongoing maintenance.
Collaborate with data engineers, analysts, and business stakeholders to make data easier to find, understand, and use.

Requirements

What you’ll need

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