Design, develop, and maintain scalable Python applications and services.
Build reusable, maintainable, and well-documented code components.
Develop backend services and APIs using Django and/or Flask.
Participate in architecture reviews and technical design discussions.
Troubleshoot and optimize production systems.
Develop and optimize large-scale data processing pipelines using PySpark.
Process, transform, and analyze large structured and unstructured datasets.
Build scalable ETL and data ingestion frameworks.
Optimize distributed data processing workloads for performance and reliability.
Implement data science algorithms and predictive models in production environments.
Collaborate with data scientists to operationalize machine learning solutions.
Develop and optimize ML pipelines using scikit-learn and related libraries.
Support model deployment, validation, and monitoring processes.
Perform complex data wrangling, cleansing, and transformation activities.
Build data pipelines utilizing Pandas, NumPy, PyArrow, and PySpark.
Develop solutions for handling large-scale datasets and analytical workloads.
Design and develop RESTful APIs supporting high-volume data operations.
Build secure, scalable services capable of handling significant throughput.
Design and optimize data access patterns across relational and NoSQL databases.
Work with efficient SQL queries and database integrations.

8+ years of professional Python development experience.
Proven experience developing enterprise-scale applications and data processing solutions.
Experience working with large-scale datasets and big data platforms.
Experience collaborating across global and distributed teams.
Expert-level Python programming skills.
Strong understanding of object-oriented programming principles.
Experience developing scalable and maintainable applications.
Strong experience with Django and Flask.
Experience building production-grade REST APIs.
Strong hands-on experience with PySpark.
Experience handling high-volume datasets.
Experience implementing and operationalizing data science algorithms.
Hands-on experience with scikit-learn.
Experience using Pandas, NumPy, PyArrow, and other Python data processing libraries.
Strong experience with relational databases: PostgreSQL, SQL Server, Oracle, MySQL.
Strong SQL development skills.
Experience with Cassandra, HBase, and other distributed NoSQL platforms.
Strong commitment to automated testing, code reviews, CI/CD, source control, and software engineering best practices.

Senior Python Data Engineer

Key skills