Develop and maintain scalable Python data pipelines, ETL processes, workflow automation, and dataset integrations. Ensure data quality, validation, performance, and production reliability while supporting analytics and machine learning teams. Work with databases, cloud data services, APIs, orchestration tools, and deployment platforms to deliver clean, production-ready data solutions.
Job Summary:
Key Responsibilities:
Technical Skills Required:
Preferred Qualifications:
We are looking for a Python Developer with strong data engineering and data handling skills. The ideal candidate will work on building scalable data pipelines, automating workflows, integrating datasets, and supporting analytics and ML teams with clean, reliable data solutions.
- Develop, optimize, and maintain data pipelines using Python.
- Work with large datasets and ensure efficient extraction, transformation, and loading (ETL).
- Integrate data from various internal and external sources.
- Build reusable Python modules and automation scripts.
- Work closely with data engineers, analysts, and stakeholders to understand requirements.
- Optimize performance of data processes and troubleshoot issues.
- Ensure data quality, validation, and integrity across systems.
- Deploy and monitor data workflows in production environments.
- Strong hands-on experience in Python (Pandas, NumPy, PySpark preferred).
- Experience with ETL development and building data pipelines.
- Good understanding of SQL, complex queries, and stored procedures.
- Experience with databases (PostgreSQL, MySQL, MongoDB, or similar).
- Exposure to big data frameworks (Spark/Hadoop) is a strong advantage.
- Experience with cloud platforms (AWS/Azure/GCP) for data services.
- Familiarity with API integrations, automation, and scheduling tools (Airflow, Cron).
- Knowledge of version control (Git) and CI/CD practices.
- Advanced experience with Python libraries used in data workflows (Pandas, NumPy, PySpark, FastAPI/Flask for data services).
- Hands-on experience with data warehousing concepts and building scalable data models.
- Practical understanding of ML model pipelines and supporting data science teams with clean, production-ready datasets.
- Experience working with workflow orchestration tools (Airflow, Prefect, Luigi).
- Familiarity with containerization and deployment (Docker/Kubernetes) for data applications.
- Strong understanding of cloud-based data services (AWS Glue, Redshift, Azure Data Factory, BigQuery).
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
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