Data Engineer

Sedgwick•Telecommuter TX, TX

About The Position

The Gen AI Engineer within the Transformation Office serves as the hands‑on architect of the enterprise data supply chain for the organization’s most advanced analytics, data science, and AI initiatives. This role performs the critical engineering work required to deliver high‑fidelity, production‑grade data that powers machine learning models, feature stores, and generative AI applications. Operating as a “day‑one” builder, the Gen AI Engineer designs and delivers data pipelines that bridge legacy on‑premise systems—including mainframes, SQL Server, and DB2—with modern cloud platforms such as Snowflake and AWS/Azure AI ecosystems. The role ensures that data is not merely transferred, but deliberately engineered to meet the statistical, performance, and governance requirements of model training, inference, and RAG‑based AI systems.

Requirements

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 (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services
Mastery of Python, SQL, and PySpark.
Deep experience with data orchestration and containerization (Docker)
Proven ability to interface with "old world" tech (on-premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption
A strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines)
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

Responsibilities

Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on‑premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), 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—including PrivateLink, IAM, network segmentation, and firewall controls—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.
Performs other duties as assigned.
Travel as required.