Gen AI Data Engineer: Multi-Cloud ML Pipelines

Sedgwick Claims Management Services Ltd; Texas; On-site; USD 120,000 - 180,000; Full time

Job summary

Sedgwick is seeking a Gen AI Engineer to join the Transformation Office. You will design and deliver data pipelines that bridge on‑premises systems with Snowflake and AWS/Azure AI ecosystems, powering advanced analytics, ML, and generative AI initiatives.

You’ll own end‑to‑end data engineering: ETL/ELT pipelines, feature stores, vector data workflows, and MLOps partnerships. Expect to optimize performance, ensure data quality, governance, and cost control across multi‑cloud environments.

Qualifications

• Master’s degree in Computer Science, Data Engineering, or related field preferred.
• Six years of hands-on data engineering experience in multi-cloud environments.
• Expert-level Snowflake architecture and Snowpipe integration.
• Advanced AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) knowledge.
• Mastery of Python, SQL, and PySpark.
• Experience with data orchestration and containerization (Docker).

Responsibilities

• Designs, builds, and maintains ETL/ELT pipelines from on-prem and cloud sources.
• Develops feature stores and analytics-ready datasets for ML workflows.
• Builds data pipelines for generative AI across multi-cloud environments.
• Leads Snowflake optimization, Snowpipe automation, and cost management.
• Implements data quality, validation, and observability frameworks.
• Collaborates with IT, security, and DBAs to ensure secure data access.

Skills

Python

SQL

PySpark

Data Modeling

Problem solving

Team collaboration

Education

Master’s degree in Computer Science, Data Engineering, or related field

Tools

Snowflake

AWS

Azure

Snowpipe

Airflow

Docker

SQL Server

Job description

Sedgwick is seeking a Gen AI Engineer to join the Transformation Office. You will design and deliver data pipelines that bridge on‑premises systems with Snowflake and AWS/Azure AI ecosystems, powering advanced analytics, ML, and generative AI initiatives.

You’ll own end‑to‑end data engineering: ETL/ELT pipelines, feature stores, vector data workflows, and MLOps partnerships. Expect to optimize performance, ensure data quality, governance, and cost control across multi‑cloud environments.