Junior Forward Deployed Engineer

SedgwickData engineering

United StatesFull-TimeJunior

Salary not disclosed

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Job Details

Have six years of hands-on data engineering experience building production-grade pipelines for Data Science and AI in multi-cloud environments, or equivalent education and experience.
Bring expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and integration with external cloud AI services.
Have advanced hands-on knowledge of AWS data services, including S3, Glue, and Lambda.
Have advanced hands-on knowledge of Azure data services, including Data Factory and Synapse.
Demonstrate mastery of Python, SQL, and PySpark.
Have deep experience with data orchestration and Docker containerization.
Be able to work with on-premise SQL, mainframe extracts, and flat files and transform data for cloud consumption.
Understand machine learning data needs, including feature engineering.
Understand generative AI data needs, including vectorization and embedding pipelines.
A master’s degree in Computer Science, Data Engineering, or a related field is preferred.

Design, build, and maintain ETL/ELT pipelines that ingest data from on-premise systems, AWS, and Azure for Snowflake and downstream AI services.
Develop feature stores and optimized datasets that are clean, versioned, reproducible, and statistically valid for Data Science teams.
Build generative AI data pipelines to extract, transform, chunk, and load structured and unstructured data into vector databases across AWS and Azure.
Implement Snowflake data modeling, Snowpipe automation, and compute and storage optimization for analytics and AI workloads.
Extract data from legacy systems while preserving system stability and avoiding disruption to core business operations.
Manage cross-platform data workflows using Airflow, AWS Step Functions, and Azure Data Factory.
Partner with IT, database, infrastructure, and security teams to resolve connectivity and access challenges and secure approval for 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.
Work with MLOps, Data Science, and AI teams to turn evolving and experimental data requirements into production-ready solutions.

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