Must have

• SQL

• Snowflake

• ELT

• ETL

• English (C1)

Nice to have

• AWS

Requirements description

✔️ Must-Have Qualifications:

• 5+ years of experience as a Data Engineer working with cloud-based ELT pipelines
• Expert-level SQL and DBT modeling skills
• Deep understanding of data warehousing concepts (dimensional modeling, incremental loading, performance optimization)
• Experience with Snowflake and cloud data architecture
• Strong communication and documentation skills – must work well in distributed teams
• Proactive attitude: ownership over data quality, incident handling and delivery discipline

✔️ Nice-to-Haves:

• Experience in the pharmaceutical or commercial and Sales data domains (CRM, HCP, field force)
• Exposure to GDPR and data privacy requirements in commercial data

Offer description

Join our Global Commercial IT team as a Senior Analytics Engineer and take part in transforming our Common Data Platform (CDP), which integrates and delivers business-critical data across 43 European and Canadian affiliates.

You’ll be joining a product team responsible for building and maintaining our AWS-based Data Lake, DBT Cloud transformation pipelines, and Snowflake data warehouse, enabling high-quality commercial and engagement insights across channels.

This role goes beyond coding – we’re looking for someone with strong technical expertise, a proactive mindset, and the ability to build foundations where there are none, improve data quality and reliability, and collaborate closely with business-facing product owner.

Your responsibilities

1.  Design, build, and maintain robust ETL/ELT pipelines in DBT Cloud, ingesting data from sources like OCE IQVIA, Adobe Analytics, Qualtrics, Salesforce CIAM, and others
2.   Contribute to the migration of legacy Snowflake and Qlik environments into the unified EUCAN CDP instance
3.   Implement data quality validation frameworks (e.g., DBT tests, freshness checks, anomaly detection)
4.   Support and improve our RUN Management process: monitoring, reruns, root-cause analysis and preventive actions
5.   Maintain and document model ownership, schema logic and transformations within our CDW_CORE > CDW_STAGE > CDW_PROD architecture
6.   Collaborate closely with Product Owner, Data Architect, and affiliate stakeholders to ensure delivery of trusted, scalable and governed data products

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