Description
By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected.
As a Gen AI Engineer within the Transformation Office, you will serve as the hands-on architect of the enterprise data supply chain for the organization's most advanced analytics, data science, and AI initiatives.
Responsibilities:
- Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on-premise systems, AWS services, and Azure platforms, centralizing and curating data for consumption in Snowflake and downstream AI services.
- Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams.
- Engineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments.
- Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads.
- Executes non-invasive data extraction strategies to unlock mission-critical data from decades-old legacy systems while preserving system stability and avoiding disruption to core business operations.
- Designs and manages complex, cross-platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory to ensure reliable, synchronized data movement across the organization's multi-cloud architecture.
- Partners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges while securing production approval for new data integrations.
- Implements automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues that could negatively impact production analytics, machine learning, or AI systems.
- Drives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance in Snowflake, AWS, and Azure, ensuring responsible cost management and measurable ROI for Transformation Office initiatives.
- Operates as a dedicated engineering partner to MLOps, Data Science, and AI teams, rapidly iterating on evolving data requirements and translating experimental use cases into scalable, production-ready data solutions.
Qualifications:
- Master's degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred.
- Six (6) years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments or equivalent combination of education and experience required.
- Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services.
- Advanced, hands-on knowledge of AWS and Azure data services.
- Mastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization.
- Proven ability to interface with old-world tech and transform it for modern cloud consumption.
- A strong understanding of the specific data needs for Machine Learning and Generative AI.
- A get-it-done attitude, capable of navigating enterprise bureaucracy and technical debt to ship code at the speed required by a Transformation Office.
- Ability to work in a team environment.
- Ability to meet or exceed Performance Competencies.