Snowflake DBT Data Engineer

LTM

$110K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience as a Data Engineer with a focus on DBT and Snowflake
  • Advanced proficiency in SQL and Jinja templating
  • Solid understanding of Snowflake architecture and features
  • Experience in data modeling, particularly dimensional models like Star Schema
  • Skilled in optimizing DBT materialization and strategies
  • Familiar with automated testing for data quality and integrity
  • Experience in CI/CD processes and orchestration tools such as Dagster

Responsibilities

  • Develop and manage end-to-end DBT projects ensuring modularity and reusability
  • Write optimized SQL queries and use Jinja for dynamic transformations
  • Design and implement dimensional data models in Snowflake
  • Configure and optimize DBT materialization strategies for performance
  • Embed data quality tests into DBT models
  • Maintain comprehensive documentation and ensure data lineage visibility
  • Integrate DBT projects with orchestration tools and establish CI/CD pipelines

Benefits

  • Collaborative work environment in a fast-paced tech backdrop
  • Opportunities for career growth and professional development
  • Exposure to the latest technologies in data engineering
  • Hands-on experience with industry-standard tools
  • Support for work-life balance with a 5-day onsite schedule
Full Job Description
Role description

Snowflake DBT Data Engineer

Location Irvine CA 5 Day onsite

Required technical skills for DBT Snowflake role

Develop DBT Projects Build maintain and manage the endtoend DBT project ensuring modularity reusability and adherence to established data standards

Advanced SQL Jinja Write complex optimized SQL queries and leverage Jinja templating within DBT models for dynamic transformations and efficient code

Snowflake Data Cloud Architecture Virtual Warehouses RoleBased Access Control Time Travel ZeroCopy Clones Objects and SQL Scripting

Data Modeling Implementation Design and implement dimensional data models eg Star Schema in Snowflake to serve business intelligence reporting and data science needs

Materialization Strategy Configure and optimize DBT materialization strategies views tables incremental models Dynamic Tables to balance data freshness query performance and Snowflake compute costs

Automated Testing Embed comprehensive data quality and integrity tests ex schema checks referential integrity directly into DBT models using DBT tests and custom checks

Documentation Lineage Maintain highquality DBT documentation and ensure complete data lineage is tracked and visible for all transformed models

Orchestration CICD Integrate the DBT project with orchestration tools eg Dagster and establish CICD pipelines eg BitBucket for reliable automated deployment of code from development to production environments"

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