Data Engineer with Databricks

Design, build, and maintain scalable data platforms that transform complex, unstructured R&D data into reliable, structured, and analytics-ready assets. This role operates at the intersection of data engineering, scientific research, and digital innovation, offering a direct impact on how data is captured, structured, governed, and utilized across the client’s laboratories worldwide.

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• Approximately 3 years of progressive experience in data engineering, preferably in R&D, energy, chemical, or similar sectors.
• Proficiency in SQL databases.
• Proficiency in Excel.
• Proficiency in Python coding.
• Hands-on experience with distributed data processing frameworks (e.g., Apache Spark, Databricks).
• Hands-on experience with Microsoft Azure (including Azure Blob Storage and Databricks).

Nice to have:

• Knowledge of Power Apps.
• Knowledge of Power BI.
• Web UI development skills.

• Transform unstructured and semi-structured R&D data into standardized, governed, and analytics-ready data assets.
• Design, build, and operate scalable ETL/ELT pipelines, data lakes, and data warehouses with strong data quality, monitoring, and lineage.
• Modernize legacy data flows through APIs and integration patterns within the Microsoft Azure ecosystem (e.g., Databricks, Blob Storage).
• Optimize cloud infrastructure for performance, security, and cost efficiency while supporting enterprise data governance and compliance.
• Partner with Data Scientists, Analysts, R&D scientists, IT teams, and vendors to enable ML/AI use cases and smart laboratory initiatives.
• Identify siloed data sources and propose strategies to extract value through connectivity.
• Collaborate with cross-functional teams to design, implement, and maintain web applications using the newly structured data.
• Contribute to Innovation Excellence standards, reusable components, and technical knowledge sharing across the whole company.