Senior Data Engineer: Snowflake & Real-Time Pipelines

Sedgwick; Lorida (FL); On-site; USD 111,000 - 200,000; Full time

Job summary

Sedgwick is seeking a Sr. Data Engineer to design, build, and maintain scalable data pipelines supporting batch, real-time streaming, and event-driven movement across on-premises and cloud sources.

You will serve as a core developer for the Snowflake data platform, optimize performance, and shape data models and semantic layers to enable Self-Service 2.0 natural-language interfaces. Collaboration across teams is essential.

Qualifications

• Bachelor’s degree in computer science, information systems, data engineering, or a related field preferred.
• Five (5)+ years of dedicated data engineering experience, with a proven record of building production-grade data pipelines, required.
• Advanced hands-on experience with Snowflake architecture, performance tuning, and data sharing required.

Responsibilities

• Designs, builds, and maintains scalable data pipelines supporting batch, real-time streaming, and event-driven data movement from on-premises and cloud sources.
• Serves as a core developer for the Snowflake data platform and supports platform performance, data modeling, and storage structures.
• Architects and builds transformation pipelines that deliver high-quality data inputs to Snowflake and dbt AI features.
• Designs and maintains semantic layers and metadata models required to support Self-Service 2.0 natural-language user interfaces.

Skills

Snowflake arch

Python

SQL

dbt

Azure Functions

Azure Data Factory

Stream Analytics

AWS data

Data pipelines

Data modeling

Data sharing

Education

Bachelor’s degree in CS/IS/Data Eng

Equivalent experience/certs

Tools

dbt Semantic Layer

Snowflake Cortex

Azure Data Factory

Job description

Sedgwick is seeking a Sr. Data Engineer to design, build, and maintain scalable data pipelines supporting batch, real-time streaming, and event-driven movement across on-premises and cloud sources.

You will serve as a core developer for the Snowflake data platform, optimize performance, and shape data models and semantic layers to enable Self-Service 2.0 natural-language interfaces. Collaboration across teams is essential.