Data Engineer Market Data Asset @ Bayer

2 days ago

Warszawa, Czech Republic Bayer Full time
  • Bachelor’s/Masters degree in Computer Science, Engineering, or a related field.
  • 3+ years of working experience in the field of Data & Analytics, preferably in the CPG industry
  • 3+ years of proficient coding experience with Python for data engineering, including SQL and PySpark (DataFrame API, Spark SQL, MLlib), with hands-on experience in various databases (SQL/NoSQL), key libraries (e.g., pandas, SQLAlchemy), parallel processing, and advanced data transformation and performance optimization techniques.
  • Ability to develop, and maintain data engineering applications using Python classes and PySpark, ensuring code modularity, reusability, and maintainability.
  • Advanced data engineering & technology knowledge (Azure Data Lake Gen2, Azure Data Factory and Databricks as well as data management knowhow (data cataloguing, data quality management).
  • Solid understanding of data modeling, ETL processing and lakehouse concepts.
  • Solid knowledge of CI/CD processes and tools (GitHub VCS, GitHub Actions, Azure DevOps Pipelines)
  • Solid data content knowledge of market data (Sell-out, Sell-through) for the CPG or health care industry which is licensable from data providers like IQVIA, Nielsen and IRI.
  • Good understanding of data governance processes to support mapping of data from multiple sources.
  • Experience working within international/intercultural teams and using Agile methodologies (Scrum, Kanban) to organize the work.
  • Strong problem solving and analytical skills. Excellent interpersonal and communication skills, active listening, consulting, challenging in a constructive way.
  • Fluent in English, both written & spoken, intercultural awareness and willingness to travel.

Data Engineer - Market Data Asset
 

The Data Assets, Analytics & AI organization at Bayer Consumer Health focuses on driving digital transformation and innovation by creating best-in-class analytical solutions that enable data-driven decision making and performance optimization for Bayer Consumer Health.

You will be part of the Data Assets, Analytics & AI organization and will be responsible for building data assets. You partner with business stakeholders, data architects, data scientists, analytics leads as well as other engineers. You will build data pipelines, data models and provision data for Analytics products and data scientists. You will follow proper development processes and contribute to the enhancement of implementation frameworks. If you are interested in joining a young and dynamic team driving the digital transformation of Bayer Consumer Health, we would like to hear from you.

,[Implement efficient data pipelines that integrate data from different sources and business domains to develop globally harmonized data models and KPI calculations by following data management and data quality standards., Make sure you follow data management and data quality standards. Ensure that data is well-managed to build stable, reusable and quality assured data assets., Ensure that data products adhere to the data protection & compliance standards. Implement effective data access management policies, data privacy policies and secure data provisioning based on corporate guidelines., Contribute to the enhancement of implementation frameworks based on the needs of the analytics products consuming your data asset and based on your own needs to process the data., Collaborate with other IT functions (enabling functions data asset teams, analytics teams, platform product managers & integration architects) to ensure the aforementioned activities are executed effectively., Collaborate with other data engineers in your team (internal or external engineers) on solving tasks and implement work packages., Estimate owned product backlog items, ensure that own deliverables are properly tested, documented and handed over to the operations team.] Requirements: Python, Degree, SQL, PySpark, API, Spark SQL, NoSQL, pandas, SQLAlchemy, Data engineering, Azure Data Lake, Azure Data Factory, Databricks, Data modeling, ETL, GitHub, VCS, Azure DevOps, Kanban, Communication skills Additionally: Training budget, Sport subscription, Private healthcare, Flat structure, Lunch card, Small teams, International projects.

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