Requirements

Expected technologies

PostgreSQL

MongoDB

Tableau

QV

Power BI

SQL

Optional technologies

Python

R

Our requirements

• Knowledge in Pharmaceutical business (ERP, CRM, Market Data)
• Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related field
• Proficiency in designing and implementing data models (conceptual, logical, and physical)
• Advanced knowledge of relational and NoSQL databases (e.g., PostgreSQL, MongoDB)
• Extensive experience with ETL tools and processes
• Proven experience in data visualization tools, specifically with Tableau, QV and Power BI.
• Strong analytical skills with a solid understanding of data analysis and interpretation.
• Strong skills in SQL and scripting
• Advanced knowledge of cloud platforms
• Excellent communication skills, with the ability to convey complex information to non-technical audiences.
• Preferred:
• Relevant certifications (e.g., GCP Certified, AWS Certified, Azure Certified)
• Strong analytical and communication skills
• Ability to work collaboratively in a team environment
• High level of accuracy and attention to detail
• Familiarity with data visualization best practices and design principles.
• Experience with other data visualization tools or programming languages (e.g., Python, R) is a plus.

Your responsibilities

• Communication:
• Communicate effectively with technical and non-technical stakeholders
• Support and host discussions within a multidisciplinary team, with potentially difficult dynamics
• Be an advocate for the team externally, and can manage differing perspectives
• Data Analysis and Data Design:
• Develop and implement and collaborate with data engineers, data analysts in integration strategies and solutions to consolidate data from multiple sources.
• Design ETL (Extract, Transform, Load) processes to ensure smooth data flow and transformation.
• Design and implement data architecture solutions that support business goals and objectives.
• Develop and maintain data models, data flow diagrams, and other documentation to support data management practices.
• Data Governance:
• Ensure data quality, integrity, and security across all data management processes.
• Monitor and optimize database performance, ensuring efficient data retrieval and storage.
• Establish and enforce data governance policies and best practices.
• Provide technical guidance and support to development teams regarding data-related issues.
• Data Innovation:
• Show an awareness of opportunities for innovation with new tools and uses of data
• Data Standards:
• Develop data standards for a specific component
• Analyze where data standards have been applied or breached, and undertake an impact analysis of that breach
• Strategic Thinking:
• Explain the strategic context of the work being performed and why it is important
• Support strategic planning in an administrative capacity
• Focus on first project will be the responsibility to replicate or reconnect the current infrastructure (ETLs, Local data warehouses and data visualization tools) used by the countries in EMEA region. The target scenario will be a global infrastructure cloud-based data platforms (AWS, Snowflake, Tableau..) combining information in two different levels:
• Global pipelines and global data models
• Local pipelines to enrich the global data models covering the necessities of the countries in terms of business.