Primary Responsibilities

• Enhance and optimize existing data models to meet business reporting and analytics needs, ensuring data consistency, accuracy, and efficiency.
• Review, improve, and optimize Python ETL processes to ensure efficient data extraction, transformation, and loading from source systems to data warehouses or lakes.
• Work closely with business stakeholders to identify data reporting and analytics requirements, ensuring data solutions are aligned with business goals.
• Utilize and manage ETL tools like Apache Airflow, NiFi, DBT, or similar to automate data pipelines and workflows.
• Identify data quality issues, performance bottlenecks, and system inefficiencies, and implement solutions to address them.
• Document processes, methodologies, and system architecture to support ongoing development and troubleshooting, as well as to ensure alignment with best practices.

Required Qualifications

• Strong experience with data modeling techniques and practices, including designing data structures and relationships that optimize reporting and analytical queries.
• Extensive experience in SQL for querying and managing data across platforms, including MS SQL Server and Google BigQuery.
• Strong expertise in Python, particularly in building and optimizing ETL processes. Familiarity with Python libraries like Pandas, SQLAlchemy, and others is essential.
• Experience with ETL automation tools such as Apache Airflow, NiFi, DBT, or similar to build scalable and maintainable pipelines.
• Familiarity with Google Cloud Platform (GCP) services, particularly in data storage, processing, and analytics tools.
• Ability to work collaboratively with both technical and non-technical stakeholders, translating complex technical concepts into understandable terms for non-technical audiences.

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