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Data Warehouse Engineer - ETL/Snowflake DB (5-8 yrs)

5-8 years

Data Warehouse Engineer - ETL/Snowflake DB (5-8 yrs)

Upraised

posted 18d ago

Job Role Insights

Flexible timing

Job Description

We are looking for a highly skilled Data Warehouse Engineer to join our dynamic team.


The ideal candidate will have hands-on experience in building, optimizing, and maintaining robust data warehouse solutions using modern tools and technologies, such as Snowflake, Kafka, and ETL frameworks.


This role is critical to ensuring the integrity, reliability, and performance of our data infrastructure to support business intelligence and analytics.

Key Responsibilities :

1. Design, build, and maintain scalable and high-performance data warehouse solutions using Snowflake.

2. Create efficient data models and schema designs that support business intelligence and analytics needs.

3. Develop, optimize, and manage ETL pipelines to transform and load data from diverse sources into the data warehouse.

4. Leverage Kafka tools for real-time and batch data streaming to ensure smooth data integration.

5. Continuously monitor and improve the performance of data warehouse queries and pipelines.

6. Implement best practices for data partitioning, clustering, and indexing.

7. Establish and enforce data governance, quality, and security standards.

8. Perform data validation, debugging, and troubleshooting of data-related issues.

9. Work closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver effective solutions.

10. Provide technical guidance and support for ad-hoc data requests.

Requirements :

1. 5+ years of experience in data engineering, data warehousing, or a related field.

2. Proven expertise in Snowflake, including database design, performance tuning, and security configurations.

3. Hands-on experience with Kafka tools for data streaming and integration.

4. Strong knowledge of ETL frameworks and tools such as Informatica, Talend, or Apache Airflow.

5. Proficiency in SQL for querying and managing data.

6. Familiarity with cloud platforms like AWS, Azure, or Google Cloud.

7. Knowledge of scripting languages like Python or Java for automation and data manipulation.


Functional Areas: Other

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