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Join the application process for the Analytics Engineer role at Spendesk . Transform data into business impact Spendesk is seeking a skilled Analytics Engineer to join our growing data organization. Reporting to the Head of Data, you will be responsible for transforming raw data into business-ready datasets, building dimensional models, and enabling self‑service analytics across the organization. About the role As an Analytics Engineer, you will bridge the gap between data engineering and data analysis/business teams by creating clean, documented, and reusable analytics assets. You’ll work closely with Data Engineers, Data Scientists, and business stakeholders to implement data quality testing, build dimensional models, and enable data‑driven decision making throughout Spendesk. This role requires an engineer who can translate business requirements into robust data models, apply best practices for data transformation, and create analytics solutions that scale with our business growth. Our tech environment dbt (core and Cloud) Snowflake Looker (original), Metabase, and Amplitude for product analytics Airbyte (cloud), Fivetran, Airflow, Hightouch, Segment for ingestion/exposure GitHub for versioning, CI/CD, and Synq for observability Key Responsibilities Transform raw data into business‑ready datasets using dbt and modern data stack tools. Build and maintain dimensional models that serve BI and product needs. Implement business logic and calculations in the data transformation layer. Create reusable analytics assets that can be leveraged across multiple use cases. Ensure data models follow best practices for performance, maintainability, and scalability. Implement comprehensive data quality testing using dbt tests. Develop and maintain data quality monitoring and alerting systems. Create data validation rules that catch issues before they impact business decisions. Establish data quality metrics and SLAs for analytics datasets. Collaborate with all stakeholders to resolve data quality issues at the source. Enable self‑service analytics by creating intuitive, well‑documented data models. Partner with business and product stakeholders to understand analytics requirements and translate them into technical solutions. Build metric definitions and calculations that ensure consistency across the organization. Provide training and support to stakeholders on analytics tools and data interpretation. Stakeholder collaboration Work closely with analysts and data scientists to provide analysis‑ready datasets. Collaborate with business stakeholders to understand requirements and design appropriate data solutions. Partner with Data Engineers to ensure optimal data pipeline design and performance. Communicate technical concepts clearly to both technical and business audiences. What we’re looking for Experience & background 3‑5 years of experience in analytics engineering, data analytics, or related data roles. Proven track record of building data models and transformations in production environments. Experience working with business stakeholders to translate requirements into technical solutions. Background in implementing data quality testing and monitoring practices. Technical requirements Expert proficiency in SQL and advanced capabilities. Hands‑on experience with dbt for data transformation and modeling. Experience with cloud data warehouses (Snowflake). Experience with data quality testing frameworks (e.g. dbt tests). Proficiency in version control systems (Git, GitHub). Understanding of dimensional modeling concepts and best practices. Analytics & business skills Strong understanding of business intelligence and analytics concepts; experience with data visualization tools and self‑service analytics platforms. Ability to translate business requirements into technical data solutions. Knowledge of statistical concepts and data analysis methodology. As we are an international team, please submit your application and CV in English. About Spendesk Spendesk is the AI‑powered spend management and procurement platform that transforms company spending. By simplifying procurement,
