Role Overview:
We are looking for a Data Engineer – Analytics & AWS with 2–4 years of experience who enjoys working across both data engineering and data analytics .
In this role, you will build and support scalable data pipelines while also working with data for analysis, data quality, transformation, and analytics use cases . You will work with contemporary AWS and Snowflake technologies and collaborate with cross-functional teams to deliver reliable, business-ready data solutions.
The ideal candidate has strong hands-on experience with AWS Glue, S3, SQL, Python, and Snowflake , along with a good understanding of ETL/ELT and data analytics.

What You'll Work On:
Data Engineering
- Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and loading.
- Build and support data pipelines using AWS Glue and Amazon S3 .
- Develop Python-based data processing and automation solutions.
- Build and optimize data warehouse solutions using Snowflake .
- Develop and support workflow orchestration using Airflow and related services.




- Work with event-driven and streaming workflows using Kafka .
- Troubleshoot production issues and improve pipeline performance, scalability, and reliability.

Data Analytics
- Analyze data to identify trends, issues, and insights.
- Use SQL and Snowflake for data analysis, validation, and investigation.
- Support data quality and reconciliation activities across multiple data sources.
- Work with business and technical teams to understand analytics requirements.
- Translate data findings into practical technical solutions.
- Support analytics and reporting use cases across multiple projects.

Key Responsibilities:
- Work on projects across Data Ingestion, Data Transformation, Data Quality, and Data Analytics .
- Develop efficient SQL queries for transformation, validation, and analytics.
- Implement and maintain Snowflake capabilities including RBAC, ABAC, Snowpipes, and Dynamic Tables .
- Develop AWS Glue ETL workflows and P

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