Job Description :

Key Responsibilities :

- Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, and processing.

- Build and optimize data warehouse solutions using Snowflake and its ecosystem components.

- Write and optimize complex SQL queries, including CTEs, window functions, and performance tuning.

- Develop and manage data transformation workflows using dbt (Data Build Tool).

- Create and maintain data processing solutions using Python.

- Develop and manage AWS-based data solutions utilizing Glue, Lambda, S3, and related services.

- Support reporting and visualization requirements through Amazon QuickSight dashboards and reports.

- Ensure data quality, integrity, and consistency across all data platforms.

- Perform root cause analysis and resolve data-related issues in a timely manner.

- Collaborate with business stakeholders, analysts, and technical teams to understand data requirements and deliver scalable solutions.

- Implement data governance, security, and compliance best practices.

- Participate in architecture discussions and contribute to data platform modernization initiatives.

- Support automation, deployment, and testing of data solutions through CI/CD practices.

Required Skills & Qualifications :

Technical Skills :

- 5-10 years of experience in Data Engineering.

- Strong hands-on experience with Snowflake Data Warehouse and its core components.

- Advanced SQL expertise, including :

1. Common Table Expressions (CTEs)

2. Window Functions

3. Query Optimization and Performance Tuning

- Strong programming experience in Python for data processing and pipeline development.

- Experience building and managing ETL/ELT pipelines.

- Hands-on experience with dbt (Data Build Tool).

- Strong knowledge of AWS services including :

1. AWS Glue

2. AWS Lambda

3. Amazon S3

- Experience with Amazon QuickSight for business intelligence and visualization.

- Experience with Rockset or similar analytical engines.

- Strong understanding of data quality management and root cause analysis.

Preferred Skills :

- Experience designing and optimizing Data Lake architectures.

- Knowledge of data security, governance, compliance, and privacy best practices.

- Familiarity with CI/CD tools and automated deployment pipelines.

- Exposure to Big Data technologies such as Spark, Hive, or related AWS services.

- Understanding of advanced analytics and data science concepts.

- Experience working in cloud-native data environments.