1. Design and develop scalable and robust data pipelines to ingest, process, and transform large volumes of data from various sources, including Oracle databases. 

2. Leverage your expertise in Oracle databases to optimize query performance, implement efficient indexing strategies, and fine-tune the data processing pipeline for enhanced data retrieval. 

3. Implement data modeling techniques to structure data in a way that optimizes performance and supports analytical needs.

 4. Build and maintain data warehouses, data lakes, and other storage systems, including Oracle-based solutions, to ensure efficient data storage, retrieval, and availability. 

5. Collaborate with data scientists and analysts to understand their data requirements and provide them with reliable and well-organized datasets for analysis and modeling, leveraging your Oracle experience to support complex financial data structures. 

6. Develop and maintain ETL (Extract, Transform, Load) processes, to integrate data from multiple sources into a unified format suitable for analysis. 

7. Optimize and tune data processing and storage systems, for improved performance, scalability, and reliability in financial services environments. 

8. Implement data governance and security measures, to ensure data integrity, confidentiality, and compliance with regulatory requirements in the financial services industry. 

9. Stay up to date with emerging technologies and industry trends in data engineering, and proactively propose innovative solutions to enhance our data infrastructure. 

10. Lead and mentor junior data engineers, providing technical guidance and sharing best practices for data engineering processes and technologies in Oracle and financial services contexts.

 11. Collaborate with cross-functional teams to identify and prioritize data engineering projects and initiatives that align with business goals and strategies in the financial services domain. 
 

1. Bachelor's or master's degree in Computer Science, Engineering, or a related field. 2. Proven experience as a Data Engineer or similar role, with a minimum of 6 years of hands-on experience in designing and implementing data solutions, preferably in the financial services industry. 3. Strong programming skills in languages such as Python, Java, or Scala, with experience in working with data processing frameworks (e.g., Spark, Hadoop). 4. Proficient in SQL and experience with relational and NoSQL databases. 5. Solid understanding of data modeling concepts and experience with data modeling tools. 6. Experience with cloud-based data technologies, such as AWS (Amazon Web Services), Azure, or Google Cloud Platform.7. Strong knowledge of data integration and ETL tools and techniques. 8. Familiarity with data governance, data security, and privacy best practices, particularly in the context of financial services. 9. Excellent problem-solving and analytical skills, with a strong attention to detail. 10. Effective communication and collaboration skills, with the ability to work in cross-functional teams. 11. Proven ability to lead and mentor junior team members.