Data Engineer

Trucordia•Lindon, UT

About The Position

Trucordia is hiring a Data Engineer for its Data & Analytics team within the Data and Technology organization. The team builds and operates a Snowflake data platform that consolidates data from acquired agencies and systems such as AMS360, Salesforce, HR, and finance. dbt models transform the data into conformed dimensions and facts, which support Power BI, curated semantic views for AI, and internal web applications. The Data Engineer will own dbt models and pipelines end to end, onboard new data sources, maintain reliable nightly runs, and improve platform performance, trustworthiness, and maintainability. The role partners with the Integrations team as well as analysts and stakeholders in Sales, Operations, and Finance. It also contributes to data governance and to the backend data layer and deployment processes for internal web applications.

Requirements

At least 3 years of professional experience building production data pipelines, including at least 2 years with a cloud data warehouse; Snowflake is strongly preferred.
Strong SQL skills, including query-plan analysis, window functions, incremental logic, and slowly changing dimensions.
Ability to profile unfamiliar schemas and perform data quality analysis.
Hands-on experience with dbt in a production project, including models, tests, macros, sources, exposures, and a CI workflow.
Proficiency in Python for data work, including loading files, calling APIs, writing small services or scheduled jobs, and testing them.
Experience with Git-based workflows, pull-request review, and CI/CD pipelines.
Sound data-modeling judgment, including the ability to explain fact-table grain and push back on requests that would compromise it.
Strong written communication skills and ability to document solutions and collaborate with technical and non-technical stakeholders.
Proven, ongoing use of AI-assisted development tools as part of daily development workflows.
Ability to use AI to accelerate coding, debugging, refactoring, and learning new technologies or codebases.
Ability to critically review AI-generated code for correctness, security, performance, and long-term maintainability.
Hands-on, habitual AI use in professional work, side projects, or personal experimentation.

Nice To Haves

Insurance brokerage or agency data experience, including policies, premium, commissions, carriers, producers, or retention.
Familiarity with AMS360, Epic, or similar agency management systems.
Experience with the Salesforce data model and replication into a warehouse.
Familiarity with Snowflake governance features such as row access policies, masking, network policies, or SCIM.
Experience with the Azure ecosystem, including Azure DevOps pipelines, Azure Functions, or Entra ID.
Experience with Power BI semantic models or feeding LLM and semantic-layer tools from a warehouse.
Experience with FastAPI or another Python web framework.

Responsibilities

Design, build, and maintain dbt models across staging, intermediate, and mart layers, including model performance, incremental strategies, tests, and documentation.
Onboard new sources into Snowflake, including profiling data, agreeing on grain and keys with analysts, and implementing loads and quality checks.
Maintain the nightly dbt run by diagnosing failures, fixing root causes, and limiting the impact of upstream source failures.
Improve observability with freshness checks, row-count and reconciliation tests, and alerting for sources or models that drift from expectations.
Tune Snowflake cost and performance, including warehouse sizing, clustering, query patterns, and task scheduling.
Contribute to the governance framework, including role grants, Entra group synchronization, row-level security policies, and access-configuration tables for internal apps.
Build the backend data layer for internal web applications and support deployments through CI pipelines.
Work with analysts and stakeholders to translate business questions into consistent data models.
Document solutions in model documentation, runbooks, and decision notes in the Azure DevOps wiki.