Junior Forward Deployed Engineer

Sedgwick•Telecommuter FL, FL

About The Position

The Junior Forward Deployed Engineer within Sedgwick’s Transformation Office serves as a hands-on architect of the enterprise data supply chain for advanced analytics, data science, and AI initiatives. The role engineers production-grade data that powers machine learning models, feature stores, and generative AI applications. It designs and delivers pipelines connecting legacy on-premise systems—including mainframes, SQL Server, and DB2—with modern cloud platforms such as Snowflake and AWS and Azure AI ecosystems. Data is engineered to meet the statistical, performance, and governance requirements of model training, inference, and retrieval-augmented generation (RAG) systems. The engineer builds and maintains ETL/ELT pipelines, feature stores, AI data workflows, and cross-platform orchestration. The role also optimizes Snowflake and cloud resources, implements quality and observability frameworks, and partners with IT, database, infrastructure, security, MLOps, Data Science, and AI teams to deliver reliable, scalable production data solutions. Other duties may be assigned, and travel is required as needed.

Requirements

Six years of hands-on data engineering experience building production-grade pipelines for Data Science and AI in multi-cloud environments, or an equivalent combination of education and experience.
Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and integration with external cloud AI services.
Advanced hands-on knowledge of AWS data services, including S3, Glue, and Lambda, and Azure data services, including Data Factory and Synapse.
Mastery of Python, SQL, and PySpark.
Deep experience with data orchestration and containerization, including Docker.
Ability to work with on-premise SQL, mainframe extracts, and flat files and transform data for modern cloud consumption.
Understanding of machine learning data needs, including feature engineering, and generative AI needs, including vectorization and embedding pipelines.
Ability to navigate enterprise bureaucracy and technical debt and deliver code at the speed required by a Transformation Office.
Ability to work in a team environment.
Ability to meet or exceed Performance Competencies.

Nice To Haves

Master’s degree in Computer Science, Data Engineering, or a related field from an accredited college or university.

Responsibilities

Design, build, and maintain resilient ETL/ELT pipelines ingesting data from on-premise systems, AWS services, and Azure platforms for Snowflake and downstream AI services.
Develop and maintain feature stores and analytically optimized datasets that are clean, versioned, reproducible, and statistically valid for machine learning workflows.
Build generative AI data pipelines, including extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure.
Implement Snowflake data models, Snowpipe automation, and compute and storage optimization for analytics and AI workloads.
Execute non-invasive data extraction strategies for legacy systems while preserving system stability and avoiding disruption to core operations.
Design and manage cross-platform data workflows with orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory.
Work with IT, database administrators, infrastructure, and security teams to address connectivity and access issues and obtain production approval for data integrations.
Implement automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues.
Optimize storage, compute usage, and query performance across Snowflake, AWS, and Azure, supporting cost management and measurable ROI.
Partner with MLOps, Data Science, and AI teams to refine data requirements and turn experimental use cases into scalable production-ready solutions.
Perform other duties as assigned.
Travel as required.