Sr Data Engineer

LegitScript•Portland, OR

About The Position

We're an innovative technology incubator seeking an experienced and forward-thinking Sr Data Engineer specializing in Generative AI to join our team. In this role, you'll spearhead the development and implementation of cutting-edge AI solutions, with a primary focus on creating a sophisticated risk detection algorithm using large language models, Generative AI techniques, and traditional machine learning methods within our SaaS environment.

Requirements

5–8+ years in a Data Engineering or Data Science role, with a proven track record of shipping models to production.
Advanced proficiency in Structured Query Language for complex data transformation and analysis.
Hands-on experience with cloud-based data platforms such as Databricks or Snowflake.
Experience with ETL and ELT tools or frameworks such as Lakeflow Declarative Pipelines, Databricks Autoloader, Informatica, Talend, or dbt.
Strong proficiency in Python, Spark/PySpark, and DABs/Terraform for data processing and pipeline development.
Strong understanding of data modeling, database design principles, and building curated datasets for analytics and operational use cases.
Experience with DevOps practices including IAC, CI/CD, Git-based development, branching strategies, and code reviews.
Proven history implementing continuous integration and continuous deployment for data pipelines and managing deployments across environments.

Nice To Haves

Familiarity with orchestration and workflow tools such as Databricks Workflows or Airflow is preferred.
Previous experience working with containerization technologies such as Docker
Proficiency with ML experiment tracking tools like MLFlow or Weights & Biases

Responsibilities

Design, build, and maintain scalable data pipelines to ingest data from disparate sources into our data warehouse/lake.
Research and develop high-performance machine learning models to solve complex business problems.
Wrap models into production-ready APIs and integrate them into our core product.
Implement automated workflows for data validation, model training, and continuous deployment (CI/CD for ML).
Monitor pipeline latency and model drift, ensuring that the system remains performant and accurate as data evolves.
Design ML models that do the heavy lifting—prioritizing tasks and automating risk assessment to make our operations smarter.
Ensure every prediction is explainable, turning "black box" code into actionable "reason codes" for our end users.
Partner directly with the teams using your tools to refine features and improve model relevance based on their feedback.
Own the success of your models by measuring their real-world efficacy, focusing on business ROI.