Snowflake Engineer - ETL Tools (5-8 yrs)

The ideal candidate will have a proven track record in data engineering with expertise in Snowflake and Kafka. In this role, you will lead a team of data engineers to design, implement, and optimize large-scale data migration and streaming projects. Your leadership skills, technical expertise, and ability to drive innovation will be crucial in ensuring the success of our data engineering initiatives.

Key Responsibilities :

Technical Leadership :

1. Lead the end-to-end design, development, and implementation of data engineering projects.

2. Provide hands-on technical guidance in leveraging the Snowflake cloud data platform and Kafka for data processing.

3. Collaborate with cross-functional teams to define project requirements, timelines, and deliverables.

Team Management :

4. Manage team dynamics and foster a collaborative environment to ensure successful project delivery.

5. Mentor and develop team members, enhancing their technical skills and fostering professional growth.

6. Coordinate team efforts to meet deadlines and maintain high-quality deliverables.

Data Architecture & Optimization :

1. Design and optimize Snowflake data architectures to support scalable and efficient data pipelines.

2. Implement and fine-tune Kafka streaming processes, ensuring high availability and fault tolerance.

3. Develop best practices for ETL workflows, data governance, and data quality assurance.

Project Execution :

1. Lead large-scale data migration and streaming projects, ensuring adherence to best practices and timelines.

2. Solve complex data engineering challenges, leveraging advanced knowledge of Kafka architecture, including producers, consumers, topics, partitions, and offsets.

3. Integrate diverse data sources into unified platforms, ensuring consistency and accuracy.

Technical Skills :

1. Programming: Proficiency in SQL and Python for data processing and analysis.

2. Hands-on experience with Snowflake cloud data platform, Kafka, and various ETL tools.

3. In-depth understanding of Kafka architecture and components, including producers, consumers, topics, partitions, and offsets.

4. Data Engineering: Strong understanding of ETL pipelines, data warehousing concepts, and cloud-based data solutions.