About You – Experience, Education, Skills, And Accomplishments
- Bachelor /master’s degree in computer science, mathematics, data science or similar related discipline with 6+ years of proven track record in systems integration and building data Transformation services.
- Experience in design, develop, and maintain robust, scalable, and efficient Python code.
- Expertise in implementing the complex data processing pipelines and algorithms.
- Analyze and interpret complex datasets to drive business insights and decision-making.
- Work closely with data scientists and analysts to ensure data quality and relevance
- Strong proficiency in Python and related libraries (e.g., Pandas, NumPy, Seaborn, MatplotLib, Flask).
- Experience with data processing, ETL, and data pipeline development.
- Knowledge of database systems (SQL and NoSQL).
- Experience with CI/CD tools such as AWS Code Pipeline or Azure DevOps
- Familiarity with containerization technologies such as Docker and Kubernetes.
It would be great if you also had . . .
- Experience with machine learning libraries and frameworks (e.g., TensorFlow, Scikit-Learn).
- Familiarity with Agile methodologies.
- Deep knowledge of math, probability, statistics and algorithms
- Outstanding analytical and problem-solving skills
- Understanding of data structures, data modeling and software architecture
- Expertise in visualizing and manipulating big datasets
What will you be doing in this role?
- Designing and developing data processing and transformation engine
- Review and optimize existing codebases to improve performance, reduce technical debt, and ensure scalability
- Python code integrates seamlessly with other systems, services, and databases
- Analyze complex datasets to extract meaningful insights, supporting decision-making processes across the organization.
- Work closely with data scientists and analysts to ensure that data models and algorithms are correctly implemented, and that data is accurate, clean, and ready for processing.
- Develop custom tools and scripts to facilitate data extraction, transformation, and loading (ETL) processes, making data accessible and usable for various business needs.
- Design and implement data processing workflows that are efficient, reliable, and scalable, leveraging Python and its ecosystem of libraries.
- Automate repetitive data processing tasks to streamline operations and reduce manual effort, allowing for faster and more consistent data handling.
- Ensure that data processing tasks are performed with a high degree of accuracy and reliability, minimizing errors and ensuring data integrity.