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Job Description
Job Description:
- We are seeking an experienced Python SQL and ETL Developer to create, construct, and manage scalable data pipelines and data integration solutions.
- The successful candidate will collaborate closely with data engineers, analysts, and business stakeholders to guarantee the delivery of reliable, high-quality data for reporting, analytics, and downstream applications.
Key Responsibilities:
- Design, develop, and uphold Python applications.
- Produce clean, efficient, and reusable Python code.
- Create data processing and automation scripts.
- Engage with databases utilizing SQL.
- Conduct code reviews and mentor junior developers.
- Identify and resolve application and data issues.
- Assist with production deployments and improvements.
- Develop, maintain, and enhance ETL pipelines for data ingestion, transformation, and loading from various sources.
- Craft efficient, scalable, and well-documented Python scripts for data processing and automation tasks.
- Design and refine complex SQL queries, views, and stored procedures for optimal performance and accuracy.
- Execute data validation, quality checks, and reconciliation processes to uphold data integrity.
- Diagnose and fix data pipeline failures and performance-related challenges.
- Collaborate with data analysts, data scientists, and business teams to clarify data requirements.
- Apply best practices for data security, governance, and compliance.
- Maintain technical documentation for ETL workflows and data models.
Technical Requirements:
- Extensive experience in Python development.
- Solid understanding of SQL and data management.
- Experience in production environments.
- High proficiency in Python for data manipulation, scripting, and automation.
- Advanced knowledge of SQL, including joins, subqueries, window functions, and performance optimization.
- Hands-on experience with ETL or ELT tools and frameworks.
- Experience with relational databases such as e
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- MySQL, PostgreSQL, SQL Server, or Oracle.
- Strong grasp of data warehousing principles, including fact and dimension tables, and star schema.
- Ability to manage large datasets and enhance data processing workflows.
- Excellent analytical, problem-solving, and debugging capabilities.
Additional Responsibilities:
- Experience with cloud platforms like AWS, Azure, or GCP.
- Familiarity with big data technologies such as Spark and Hadoop.
- Exposure to workflow schedulers like Airflow, Prefect, or Cron.
- Knowledge of CI/CD pipelines and version control systems like Git.
- Understanding of data governance and lineage tools.