Data Engineer
New
S
SkimmerData Engineering
Skimmer has downtown office spaces in Austin, TX and Toronto, ON, and remote-first employees around the US. Right now we can hire hybrid employees in Austin and Toronto, or remote employees in the following states: Alabama, California, Florida, Georgia, Illinois, Indiana, Kansas, Kentucky, Maryland, Michigan, North Carolina, Ohio, Oregon, Rhode Island, Tennessee, Texas, and Virginia.Full-TimeMiddle
SalaryCompetitive base pay + bonus potential
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Job Details
- Required Skills
- PythonSQLETLSnowflakeC#Data modelingdbt
Requirements
- Demonstrated experience in data engineering, with a track record of owning data infrastructure end to end
- Strong SQL skills and hands-on experience with a cloud data warehouse (Snowflake preferred)
- Proficiency in Python for data transformation and automation
- Experience working with C#/.NET application environments and partnering with application engineers on source systems
- Experience with managed ingestion and transformation tooling (Fivetran, dbt, or similar)
- Hands-on experience building data models and reports in a BI/analytics platform (Sigma Computing a strong plus), ideally embedded in a customer-facing product
- Proficiency with AI coding tools like Claude or Cursor
- Solid understanding of data modeling concepts and ELT/ETL best practices
- Strong communication skills and comfort working with both technical and non-technical partners
Responsibilities
- Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
- Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
- Manage and extend ingestion using Fivetran across a growing set of sources
- Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
- Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
- Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
- Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
- Document data models, definitions, and architecture to support a growing team
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