Senior Data Engineer: Scala, Spark & Snowflake

Sedgwick; Iowa Center (IA); On-site; USD 111,000 - 200,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) matching

Paid time off

Job summary

Sedgwick is seeking a Senior Data Engineer to design and maintain scalable data pipelines, primarily focused on Snowflake-based data platforms and dbt AI features.

You will lead transformation workflows, design semantic layers, and guide junior engineers while collaborating with cross‑functional teams to ensure data quality and governance. A strong emphasis on cloud data tools and performance is essential.

Qualifications

• Five+ years of dedicated data engineering experience building production-grade data pipelines.
• Advanced Snowflake architecture, performance tuning, and data sharing experience.
• Experience moving data across hybrid environments (on‑premises to cloud) 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 the Snowflake data platform, supporting performance, modeling, and storage structures.
• Architects and builds transformation pipelines delivering high-quality data to Snowflake and dbt AI features.
• Designs semantic layers and metadata models to support Self-Service 2.0 NL interfaces.
• Establishes reference architectures, reusable templates, and CI/CD practices for decentralized teams.

Skills

Python

SQL

Data pipelines

Data modeling

Mentorship

Education

Bachelor’s degree in computer science or related field

Tools

Snowflake

dbt

Azure Data Factory

Azure Functions

Stream Analytics

AWS data tools

Job description

Sedgwick is seeking a Senior Data Engineer to design and maintain scalable data pipelines, primarily focused on Snowflake-based data platforms and dbt AI features.

You will lead transformation workflows, design semantic layers, and guide junior engineers while collaborating with cross‑functional teams to ensure data quality and governance. A strong emphasis on cloud data tools and performance is essential.