1.Data analysis: Ability to analyze complex data sets, identify trends, and draw meaningful insights.

2. Programming: Strong programming skills in languages like Python, Java, R, and Scala are essential for data processing and analysis.

3. Data warehousing: Understanding of data warehousing concepts and expertise in working with tools like Hadoop, Spark, and NoSQL databases.

4. Data visualization: Ability to create clear and effective visualizations to communicate insights and findings to stakeholders.

5. Machine learning: Knowledge of machine learning algorithms and their application in big data analytics.

6. Data modeling: Understanding of data modeling and schema design to optimize data storage and retrieval.

7. Cloud computing: Knowledge of cloud computing platforms like AWS, Azure, and Google Cloud, and their integration with big data technologies.

8. Data security: Understanding of data security concepts and experience implementing security measures to protect sensitive data.

9. Data governance: Knowledge of data governance principles and best practices, including data quality, metadata management, and compliance.

10. Communication: Strong communication skills to work effectively with cross-functional teams and communicate findings to stakeholders with varying levels of technical knowledge.