Key Responsibilities:
Design, develop, and maintain scalable ETL processes for data transformation and integration.
Build and manage data pipelines to support analytics and operational needs.
Ensure data accuracy, integrity, and consistency across various sources and systems.
Collaborate with data scientists and analysts to support AI model deployment and data-driven decision-making.
Optimize data storage solutions, including data lakehouses and databases, to enhance performance and scalability..
Monitor and troubleshoot data workflows to maintain system reliability.
Stay updated with emerging technologies and best practices in data engineering.
3+ years of experience in data engineering or a related role within a production environment.
Proficiency in Python and SQL
Experience with both relational (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB, Elasticsearch).
Familiarity with big data AWS tools and frameworks such as Glue, EMR, Kinesis etc.
Experience with containerization tools like Docker and Kubernetes.
Strong understanding of data warehousing concepts and data modeling.
Excellent problem-solving skills and attention to detail.
Strong communication skills, with the ability to work collaboratively in a team environment.

















