• Data Pipeline Monitoring and Troubleshooting: Ensuring that all data pipelines are operating correctly and addressing any issues promptly.
• Data Quality Assurance: Implementing and overseeing processes to ensure the accuracy and integrity of data.
• Query Optimization: Continuously optimizing queries and data retrieval methods for performance and efficiency.
• Integration of New Data Sources: Adding and integrating new data sources into the existing data architecture.
• Automated Script Execution and Monitoring: Running and monitoring automated scripts for data extraction, transformation, and loading (ETL).
• Collaboration with Stakeholders: Communicating with business analysts, data scientists, and other stakeholders to understand data needs and requirements.
• Performance Reviews of Data Systems: Evaluating the performance of databases, data lakes, and other storage systems.
• Data Cleaning and Transformation: Regularly cleaning and transforming data to maintain its usefulness and relevance.
• Report Generation: Creating and updating routine data reports for internal use or for stakeholders. 
• Code Reviews and Updates: Reviewing and updating ETL scripts and data pipeline code for improvements and efficiency.
• Data Modeling and Database Design: Developing and refining data models, and optimizing database designs for performance and scalability.
• Documentation and Knowledge Sharing: Updating documentation for data pipelines and databases, and sharing knowledge with the team.

Experience

Relevant Work Experience: Hands-on experience in a data engineering role, demonstrating skills in managing large datasets and performing complex data processing tasks

Educational Background

• Degree in Computer Science, Engineering, Mathematics, or a related field

Technical Skills

• Programming Languages: Proficiency in languages like Python( or Java, or Scala).
• Database Management: Deep understanding of SQL and experience with relational databases, as well as NoSQL databases like MongoDB.
• Big Data Technologies: Familiarity with big data tools like Apache Hadoop, Spark, Kafka.
• ETL Tools: Experience with ETL tools and processes.
• Data Modeling: Knowledge of data modeling and understanding of different data structures.
• Understanding of Data Warehousing Solutions: Familiarity with data warehousing concepts.
• Data Visualization Tools: Skills in data visualization tools like Tableau or Power BI can be beneficial, as they may be required to present data insights effectively.