Must have

• SQL

• dbt

• Snowflake

• Cloud

• Polish

Requirements description

Must-Have Qualifications

• 5+ years of experience as a Data Engineer (or similar role) working with cloud-based ELT pipelines.
• Expert-level SQL and DBT data modeling skills.
• Deep understanding of data warehousing concepts: dimensional modeling, incremental loading, performance optimization.
• Hands-on experience with Snowflake and modern cloud data architectures.
• Strong communication and documentation skills – proven ability to work effectively in distributed teams.
• Proactive mindset with ownership over data quality, incident resolution, and disciplined delivery.

Offer description

Senior Analytics Engineer (DBT Cloud, Snowflake)

We are looking for a highly skilled Senior Analytics Engineer to join our data team and drive the design, development, and governance of enterprise-grade data pipelines. In this role, you will work with cutting-edge cloud technologies, collaborate with international stakeholders, and ensure that high-quality, trusted data is available for strategic decision-making across the organization.

Tech Stack & Environment

• DBT Cloud – core ELT engine
• Snowflake – data warehouse
• AWS S3 – data lake / ingestion zone
• GitHub, Azure DevOps – version control & CI/CD
• Data sources: IQVIA OCE, Salesforce Marketing Cloud, Adobe AEM/Analytics, Shopify, Qualtrics, Accutics, Facebook, LinkedIn, Instagram

This role offers the opportunity to shape enterprise-scale data solutions, work with cutting-edge cloud technologies, and collaborate with a highly skilled, international team. If you are passionate about building trusted, high-quality data products that power strategic decisions, we’d love to hear from you.

Your responsibilities

1. Design, build, and maintain efficient ETL/ELT pipelines in DBT Cloud, ingesting data from sources such as IQVIA OCE, Adobe Analytics, Qualtrics, Salesforce CIAM, and others.
2. Contribute to the migration of legacy Snowflake and Qlik environments into the unified EUCAN CDP platform.
3. Implement robust data quality validation frameworks (including DBT tests, freshness checks, anomaly detection).
4. Support and enhance the RUN Management process: monitoring, job reruns, root cause analysis (RCA), and preventive action implementation.
5. Maintain and document model ownership, schema logic, and transformation processes within the CDW_CORE → CDW_STAGE → CDW_PROD architecture.
6. Collaborate closely with the Product Owner, Data Architect, and affiliate stakeholders to deliver trusted, scalable, and well-governed data products.

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