Sr. Data Engineer- Analytics at Insight Global
Arden Hills, MN
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Jul 27, 2026
Arden Hills, MN
Jul 29, 2026
What this job asks for AI summary
A Senior Analytics Data Engineer role focused on designing, building, and maintaining data models and ELT pipelines that support marketing analytics and business decision-making. The position requires deep expertise in dbt and Snowflake, strong dimensional modeling skills, and familiarity with marketing and customer data. The engineer will collaborate closely with marketing stakeholders and a data engineering team to deliver well-documented, scalable analytical data products.
Senior level · 5+ years
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $142,568 (middle half $111,775–$173,013).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 29, 2026. It is a model, not a headcount.
Why we read it this way (5)
No work location or remote policy is stated in the posting; CBSA and state fields are left blank. The role is posted through Insight Global (a staffing firm), so the actual employer and location are unknown.
The degree requirement lists a Bachelor's in a relevant field 'or equivalent practical experience,' so no hard degree gate is set.
SOC classification is a close call: the role's primary work is building dbt models, ELT pipelines, and dimensional schemas in Snowflake (data/analytics engineering), which maps best to 15-1243 Database Architects. However, the pipeline-building and CI/CD emphasis also has meaningful overlap with 15-1252 Software Developers.
Several skills listed in the requirements (dimensional data modeling, data warehousing principles, data governance, marketing/customer data experience) are methodologies or domain knowledge rather than named tools and are therefore not emitted as discrete skills per extraction rules.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): dimensional data modeling.
Read the full posting
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