Senior Data Engineer - Snowflake & Real-Time Pipelines

Sedgwick; Alabama; On-site; USD 111,000 - 200,000; Full time

Job summary

Sedgwick is seeking a Senior Data Engineer to design and maintain scalable data pipelines, primarily around Snowflake and dbt, and to guide data teams in cloud-based data engineering practices.

The role emphasizes building production-grade data platforms, semantic layers, and CI/CD-driven development across on-premises to cloud environments. Strong leadership and communication are essential.

Qualifications

• Bachelor's degree in computer science, information systems, data engineering, or related field preferred.
• 5+ years of dedicated data engineering experience building production-grade pipelines.
• Strong Snowflake architecture, performance tuning, and data sharing experience.
• Experience moving data across hybrid on-premises to cloud environments using batch, streaming, and event-driven patterns.

Responsibilities

• Designs, builds, and maintains scalable data pipelines for batch, real-time streaming, and event-driven movement.
• Core developer for Snowflake data platform; optimize performance and storage structures.
• Architects transformation pipelines delivering high-quality data inputs to Snowflake and dbt features.
• Designs semantic layers and metadata models to support Self-Service 2.0 NLP interfaces.
• Establishes reusable templates and CI/CD practices for safe, decentralized data product development.
• Mentors one data engineer and guides data analysts in engineering practices.

Skills

Snowflake architecture

Python

SQL

dbt

Azure data tools

Data pipelines

CI/CD

Data modeling

Real-time streaming

Cross-functional leadership

Education

Bachelor's degree in Computer Science or related field

Tools

Snowflake

dbt Semantic Layer

Azure Data Factory

Azure Functions

Stream Analytics

Job description

Sedgwick is seeking a Senior Data Engineer to design and maintain scalable data pipelines, primarily around Snowflake and dbt, and to guide data teams in cloud-based data engineering practices.

The role emphasizes building production-grade data platforms, semantic layers, and CI/CD-driven development across on-premises to cloud environments. Strong leadership and communication are essential.