What you'll do :

• Design, develop, and maintain efficient ETL pipelines to process and transform large volumes of data from various sources.
• Implement data transformation and cleansing processes to ensure high-quality data.
• Assist in building and optimizing scalable data warehouse solutions using Snowflake, Data bricks, Redshift, or similar technologies.
• Ensure that data models are efficient, scalable, and support the needs of analytics teams.
• Utilize AWS Glue and PySpark for processing and transforming large datasets in the cloud.
• Implement data pipelines using Apache Airflow for scheduling and orchestration.
• Manage and optimize relational databases (RDBMS) to support both transactional and analytical workloads.
• Write efficient SQL queries to support data extraction, reporting, and analytics.
• Work closely with your lead and other stakeholders to understand data requirements and deliver reliable data solutions.
• Contribute to the design and architecture of data systems, ensuring alignment with business needs and technical best practices.
• Ensure data integrity, accuracy, and availability through rigorous testing, validation, and monitoring of ETL processes.
• Support data governance initiatives by implementing best practices for data management and security.

What makes you  a great fit :.

• 6-9 years of experience in data engineering, with a strong focus on ETL processes and data warehousing.
• Hands-on experience with ETL tools and frameworks, as well as Python and PySpark for data processing.
• Experience with cloud-based data platforms such as AWS Glue, Snowflake, Databricks, or Redshift.
• Proficiency in SQL and experience with RDBMS platforms (e.g., MySQL, PostgreSQL, Oracle).
• Familiarity with data orchestration tools like Apache Airflow.
• Strong understanding of data warehousing concepts and best practices.
• Experience in building and optimizing data pipelines and workflows.
• Knowledge of cloud infrastructure (AWS, Azure, GCP) and data processing technologies.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Ability to work in a fast-paced environment and manage multiple tasks simultaneously.
• Experience with machine learning pipelines and big data processing.
• Familiarity with DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines.
• Certification in AWS or other relevant cloud platforms.