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Data Engineering Manager
Sandoz
posted 1mon ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
- ETL Development: Design, implement, and maintain scalable ETL pipelines to extract, transform, and load data from various sources into data warehouses or data lakes.
- Databricks Utilization: Leverage Databricks to develop, optimize, and scale data engineering workflows.
- Data Modelling: Design and implement robust data models to support analytics and reporting needs.
- SQL Proficiency: Write, optimize, and maintain complex SQL queries for data extraction and transformation tasks.
- PySpark Development: Utilize PySpark for big data processing tasks, developing scalable and efficient solutions.
- Data Quality and Validation: Implement data quality checks and validation processes to ensure data accuracy and consistency.
- Data Integration: Integrate data from multiple sources, ensuring data consistency and reliability.
- Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
- Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and related processes. Performance Optimization: Optimize data processing performance by fine-tuning ETL pipelines and data storage solutions.
- Security and Compliance: Ensure compliance with data governance policies and security protocols to protect sensitive and confidential information.
- Continuous Improvement: Stay current with industry trends and emerging technologies, continuously improving data engineering practices.
- Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills with senior stakeholders
- Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and their data services (Databricks). Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Knowledge of real-time data processing and streaming technologies.
- Proficiency in data visualization tools (e.g., Tableau, Power BI). Experience with DevOps practices and tools for CI/CD pipelines.
- Bachelors or master s degree in computer science, Engineering, Information Systems, or a related field.
- 6+ years experience in a Global company as a data steward, engineer, modeler or data scientist, with a strong focus on ETL tools, SQL, and PySpark.
- Business understanding of pharmaceutical industry and data standards. Domain experience in at least one of the following areas - a) Pharma R&D, b) Manufacturing, Procurement and Supply Chain and c) Marketing and Sales. Experience in working in Pharma / Life Science industry strongly preferred
- Strong proficiency in SQL and experience with database management systems (e.g., PostgreSQL, MySQL, Oracle). Hands-on experience with Databricks for developing and optimizing data workloads.
- Proficiency in PySpark for big data processing tasks.Knowledge of data warehousing solutions (e.g. Snowflake). Experience of working with large codebase / repos using Git / Bitbucket
Employment Type: Full Time, Permanent
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