Senior Data Engineer

Enlyte•UNAVAILABLE, UNAVAILABLE

2d•Remote

About The Position

As a Senior Data Engineer in the Casualty Analytics Group, you will lead the design and buildout of the data infrastructure that powers Enlyte’s analytics and decision-making capabilities. You will join a growing engineering team, set architectural direction for data pipelines integrating multiple source systems into the Snowflake data warehouse, define patterns and standards, and shape how the team scales.

Requirements

At least 6 years of hands-on experience building data infrastructure and pipelines in production cloud environments.
Expert-level SQL proficiency, including performance tuning and complex window functions.
Strong working knowledge of Python for data pipeline development and automation.
Deep technical experience with modern data stack technologies, including Snowflake, dbt, data ingestion tools, orchestration tools, and AI-assisted development tools.
Extensive experience with OLTP databases such as PostgreSQL, Oracle, and SQL Server, and a strong understanding of the differences between OLTP and OLAP systems.
Experience working with AWS cloud services for data workloads, such as S3, IAM, networking, and Lambda.
Experience defining and enforcing CI/CD practices for data pipelines, including automated testing, version control workflows, and deployment patterns.
Experience with data modeling, such as Star Schema, One Big Table, or Data Vault.
Excellent communication skills, including the ability to explain technical concepts to non-technical stakeholders and influence technical direction across teams.
Self-directed problem-solving ability; thrives in ambiguity, builds from the ground up, and influences across organizational boundaries without formal authority.

Responsibilities

Build and maintain production data pipelines using Snowflake and dbt to integrate and transform data from sources such as SQL Server, Oracle, PostgreSQL, and Salesforce.
Design and establish core data architecture, including patterns and frameworks for production pipelines.
Partner with business stakeholders to identify and prioritize high-value data integration opportunities.
Establish and enforce engineering standards through documentation, design reviews, and code reviews.
Make technology and governance recommendations and implement frameworks that balance immediate needs with long-term scalability.
Mentor and lead team members as the department grows.
Lead proof-of-concepts and MVPs to demonstrate value and secure support for larger data initiatives.