Senior Data Engineer [Remote-US]

We're looking for a Senior Data Engineer to help the team deliver data science services and streaming data pipelines. Over the course of your time with Quanata, you will have the opportunity to develop data pipelines that bring together data from multiple sources to evaluate insured risk, deliver services that enable data science models, and enable business and partner access to key datasets.

1. Design and build robust data pipelines that integrate data from diverse sources to support risk evaluation and business intelligence.
2. Build streaming data pipelines using Kafka and AWS services to enable real-time data processing and decision-making.
3. Create and operate data services that make curated datasets accessible to internal teams and external partners through APIs and scheduled deliveries.
4. Support data science workflows by building infrastructure that enables model training, feature engineering, and production inference.
5. Implement data quality frameworks including validation, monitoring, and alerting to maintain trust in our data assets.
6. Contribute to CI/CD pipelines and infrastructure-as-code practices to ensure reliable, repeatable deployments.
7. Deliver highly reliable services by following engineering best practices and participating in an on-call rotation to support production systems.
8. Contribute to the data warehouse by creating well-modeled datasets in Snowflake that empower analysts and data scientists.
9. Collaborate with data scientists, analysts, and business stakeholders to understand requirements and deliver solutions that drive value.

• Bachelor's degree or equivalent relevant experience and;
• 6-8 years of industry experience in data engineering or software engineering with a focus on data systems.
• Strong proficiency in Python and SQL for data pipeline development and analysis.
• Experience with AWS data platforms (Glue, S3, Athena, Lambda, Step Functions).
• Familiarity with Snowflake or similar cloud data platforms.
• Familiarity with streaming technologies such as Kafka or Kinesis.
• Experience with workflow orchestration tools (Airflow, Step Functions, or similar).
• Proficiency in infrastructure-as-code tools like Terraform.
• Understanding of data modeling principles.
• Excellent written and verbal communication with a strong collaborative focus.
• Experience with CI/CD tailored for machine learning systems (e.g., automating model training, validation, and deployment)

• Prior experience working within the insurance industry or another highly regulated environment, demonstrating an understanding of pertinent regulatory, security, and data governance challenges.
• Familiarity with ML infrastructure and supporting data science teams in productionizing models.
• Experience designing and building feature stores or real-time feature serving systems.
• Exposure to data governance and cataloging tools (e.g., DataHub, Atlan, or similar).