DE (SQL+Python+Data Pipeline)

Write, optimize, and maintain complex SQL queries for data extraction, transformation, and reporting. 

Work with relational databases and cloud data warehouses such as Snowflake and Redshift. 

Apply data modeling principles, including normalization and understanding of various data types. 

Utilize Python along with Pandas or PySpark for data processing and analysis. 

Assist in the development and maintenance of ETL/ELT pipelines to manage large-scale data workflows. 

Collaborate with analysts, engineers, and business teams to ensure clean, consistent, and accessible data. 

Gain exposure to NoSQL databases such as MongoDB or DynamoDB. 

Use cloud platforms, preferably AWS, for managing and accessing data services. 

Create reports and dashboards using visualization tools like Tableau, Power BI, or Looker. 

Support initiatives involving real-time data streaming and help manage data streaming platforms (e.g., Kafka, Kinesis). 

1+ years of experience working with SQL and databases. 

Strong proficiency in SQL and understanding of data modeling and data warehousing concepts. 

Basic experience with Python and data frame libraries like Pandas or PySpark. 

Familiarity with cloud-based data warehouse platforms like Snowflake and Redshift. 

Understanding of NoSQL databases and unstructured data handling. 

Exposure to ETL/ELT tools and practices. 

Awareness of cloud platforms, especially AWS and its data services. 

Working knowledge of data visualization tools such as Tableau, Power BI, or Looker. 

Interest in or basic understanding of real-time data streaming platforms.