Junior AI Data Engineer: Build Production Data Pipelines

Sedgwick; Florida; On-site; USD 120,000 - 190,000; Full time

Job summary

Sedgwick in Florida is seeking a Junior Forward Deployed Engineer to design and build data pipelines powering our AI initiatives. You will bridge on-prem legacy systems with Snowflake, AWS, and Azure, delivering production-grade data for model training, inference, and RAG-based AI workloads.

You will develop ETL/ELT pipelines, manage feature stores, and collaborate with Data Science, MLOps, and security teams to maintain quality, governance, and cost efficiency in a multi-cloud environment.

Qualifications

• Master's degree in Computer Science, Data Engineering, or related field preferred.
• Six years of hands-on data engineering experience building production-grade pipelines for Data Science/AI.
• Proficiency with Snowflake architecture, data sharing, performance tuning, and Snowflake integration with external AI services.
• Strong Python, SQL, PySpark, and data orchestration skills; experience with on-prem legacy systems.

Responsibilities

• Designs, builds, and maintains ETL/ELT pipelines from on-prem and cloud sources.
• Develops feature stores and optimized datasets for ML workflows.
• Builds data pipelines for generative AI use cases and vector databases across AWS/Azure.
• Leads Snowflake optimization, Snowpipe automation, and multi-cloud analytics.
• Works with IT/security to ensure governance, access, and compliance.

Skills

Python

SQL

PySpark

Data orchestration

Teamwork

Problem solving

Education

Master's degree in Computer Science or Data Engineering

Tools

Snowflake

AWS

Azure

Snowpipe

Airflow

Azure Data Factory

Synapse

Docker

Job description

Sedgwick in Florida is seeking a Junior Forward Deployed Engineer to design and build data pipelines powering our AI initiatives. You will bridge on-prem legacy systems with Snowflake, AWS, and Azure, delivering production-grade data for model training, inference, and RAG-based AI workloads.

You will develop ETL/ELT pipelines, manage feature stores, and collaborate with Data Science, MLOps, and security teams to maintain quality, governance, and cost efficiency in a multi-cloud environment.