Senior Data Engineer: Snowflake Platform Lead

Sedgwick; Texas; On-site; USD 120,000 - 180,000; Full time

Job summary

Sedgwick in Texas seeks a Sr. Data Engineer to design, build, and maintain scalable data pipelines across on-premises and cloud sources, powering batch, streaming, and event-driven data movement with Snowflake and dbt.

You'll mentor one data engineer, guide analytics colleagues, and drive best practices in data modeling, semantic layers, and CI/CD while evaluating Azure and AWS data tools to optimize data flow and support AI-ready data products.

Qualifications

• Five+ years of dedicated data engineering experience.
• Advanced Snowflake architecture and performance tuning experience.
• Experience moving data across hybrid environments (on-prem to cloud).
• Experience with semantic layers and data for AI/NLP use cases.

Responsibilities

• Designs, builds, and maintains scalable data pipelines for batch, real-time streaming, and event-driven data movement.
• Serves as a core developer for the Snowflake data platform and supports performance and data sharing.
• Architects transformation pipelines delivering high-quality inputs to Snowflake and dbt AI features.
• Maintains semantic layers and metadata models to support natural-language interfaces.
• Establishes reusable templates, CI/CD practices, and reference architectures for data products.
• Provides direct management and mentorship to one data engineer and guides analysts.

Skills

Leadership & mentoring

Cross-functional collaboration

Analytical thinking

Problem solving

Education

Bachelor's degree in computer science or related field

Tools

Snowflake

dbt

Azure Data Factory

Azure Functions

Stream Analytics

SQL

Python

AWS

Job description

Sedgwick in Texas seeks a Sr. Data Engineer to design, build, and maintain scalable data pipelines across on-premises and cloud sources, powering batch, streaming, and event-driven data movement with Snowflake and dbt.

You'll mentor one data engineer, guide analytics colleagues, and drive best practices in data modeling, semantic layers, and CI/CD while evaluating Azure and AWS data tools to optimize data flow and support AI-ready data products.