Develop and maintain scalable data pipelines to integrate data from production equipment, operational systems and business applications across on-premises and cloud environments.
Develop and maintain scalable data pipelines to integrate data from production equipment, operational systems and business applications across on-premises and cloud environments.
Implement and manage ETL processes with automated workflows to ensure data accuracy and consistency.
Collaborate with cross-functional teams to optimize data storage solutions and support data-driven initiatives.
Use Databricks for data processing and analytics; monitor, troubleshoot, and optimize pipelines for performance and reliability.
Support data quality and governance across platforms; collaborate with data scientists, analysts and stakeholders.
Requirements
Nice-to-have: AWS or Azure experience; Apache Airflow; Apache Spark; data lakehouse architecture; NoSQL; background in semiconductor/manufacturing.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
1+ years of hands-on data engineering experience in production, ideally with hybrid on-premises and cloud infrastructure.
Proficiency in Python and SQL; experience with relational databases (e.g., Microsoft SQL Server).
Knowledge of data modeling, schema design and normalization; experience with Databricks or similar platform.
Familiarity with Git and data governance principles.
Salary
Not disclosed
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