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Appinio

Analytics Engineer

Appinio

. Design, build, and maintain clean, scalable data models in the data warehouse using SQL and dbt .

Posted 11/9/2026full-timeRemote • Spain, Germany, United Kingdom, Portugal, NetherlandsMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in SQL and dbt for designing and maintaining scalable data models and transformation workflows. Proficient in building and troubleshooting data pipelines while ensuring data quality and collaborating effectively with stakeholders.

Highest-signal resume keywords
SQLDbtData Pipeline DevelopmentCloud-Based Data WarehousesPython

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
Data ModelingData TransformationAutomated TestingData ValidationPerformance OptimizationData Quality PrinciplesAnalytics-Ready DatasetsRoot Cause AnalysisBusiness Metrics DefinitionAI-Powered Tools
Soft Skills
Analytical ThinkingDetail-OrientedTechnical CommunicationCollaboration
Tools & Technologies
Amazon RedshiftSnowflakeBigQueryAPIsData Warehouses
Industry Keywords
Data EngineeringBusiness IntelligenceData AnalyticsData Integration

Tech Stack

Tools & technologies
Amazon RedshiftBigQueryCloudPythonSnowflakeSQL

About the role

Key responsibilities & impact
  • Design, build, and maintain clean, scalable data models in the data warehouse using SQL and dbt
  • Develop and maintain data transformation workflows that turn raw data into analytics-ready datasets
  • Build, maintain, and troubleshoot data pipelines and integrations from APIs, databases, and third-party systems
  • Implement automated tests, validations, monitoring, and proactive issue resolution for data pipelines and models
  • Document data models, pipelines, business definitions, and transformation logic
  • Define, implement, and standardize business metrics such as revenue, bookings, and churn
  • Collaborate with analysts, business stakeholders, and engineering teams to deliver reliable data solutions
  • Optimize SQL queries, dbt models, and data pipelines for performance and scalability
  • Explore AI-powered tools to automate repetitive tasks and improve data engineering and analytics workflows
  • Prepare data foundations for AI-driven analytics through structured models, definitions, and documentation
  • Report directly to the Team Lead, Business Intelligence

Requirements

What you’ll need
  • Strong SQL skills and hands-on experience designing, building, and maintaining scalable data models in a modern data warehouse
  • Practical experience using dbt to develop, test, document, and maintain data transformation workflows
  • Proficiency in Python
  • Experience building, maintaining, and troubleshooting data pipelines, including integrations with APIs, databases, and third-party systems
  • Knowledge of modern data warehouse architectures
  • Experience with cloud-based data warehouses such as Amazon Redshift, Snowflake, or BigQuery
  • Understanding of data quality principles, automated testing, validation, and monitoring
  • Ability to translate business requirements into reliable data models and clearly defined metrics
  • Analytical, detail-oriented approach and ability to investigate root causes and develop sustainable solutions
  • Ability to communicate technical concepts clearly and collaborate with technical and non-technical stakeholders
  • Interest in AI-powered development tools and workflow automation
  • Application must be submitted in English

Benefits

Comp & perks
  • Flexibility Policy: average of 30 vacation days taken, with no hard cap on the number of vacation days
  • Temporary work from abroad: EU-based employees can work outside their country of residence for up to 180 days per year
  • All necessary hardware, including a personal MacBook
  • Deutschland ticket or NAVIT mobility budget for employees located in Hamburg or Berlin
  • Subsidised Urban Sports Club or ClassPass membership, based on location
  • Access to co-working spaces in Hamburg, Berlin, Munich, London, Madrid, Barcelona, or New York, based on location