Analytics Engineer

Niche

. Build and support scalable data models that translate raw data into clean, business-ready datasets .

Posted 9/18/2026full-timeRemote • Arizona • United StatesMid-LevelSenior💰 $96,000 - $120,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable data models, ensuring data accuracy, and implementing data governance practices. Proficient in advanced SQL, dbt, and dimensional data modeling techniques to support self-service analytics and reporting.

Highest-signal resume keywords

Advanced SQL ProficiencyDimensional Data ModelingDbt ExperienceData Governance IntegrationAnalytics Engineering Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Transformation WorkflowsKimball MethodologyStar SchemaSemantic ModelsAutomated TestingData ModelingCode DocumentationPerformance TuningLineage TrackingBusiness Metrics Modeling

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

SnowflakeLookerTableauGitGoogle AnalyticsCRM/Salesforce

Industry Keywords

Business IntelligenceData GovernanceModern Data StackSelf-Service AnalyticsBI Integration

Tech Stack

Tools & technologies

PythonSQLTableau

About the role

Key responsibilities & impact

Build and support scalable data models that translate raw data into clean, business-ready datasets
Ensure data accuracy, documentation, observability, semantic consistency, and performance
Develop and maintain modular dbt models, semantic layers, and BI views for business analysts and executive stakeholders
Understand evolving business trends, business logic, and cross-functional partnerships to embed context and governance in the data layer
Develop, document, and maintain Kimball dimensional models, semantic definitions, warehouse transformation logic, and BI datasets
Design, build, and maintain version-controlled data models supporting self-service analytics and reporting
Ensure data quality and pipeline accuracy through automated dbt testing, lineage tracking, performance tuning, and proactive alerting
Improve complex business logic and fragmented queries by standardizing analyst requests into documented semantic models
Integrate data governance, metric standardization, documentation, single-source-of-truth definitions, and secure data access
Collaborate with data team members and stakeholders; participate in standups, planning, and retrospectives
Troubleshoot data logic issues and modeling errors and participate in analytics support activities
Identify improvements to data transformation processes and semantic definitions
Contribute to the analytics platform, dbt models, semantic layers, and BI reporting frameworks
Independently own analytics engineering workstreams and modeling roadmap items
Develop domain expertise in business metrics and data definitions

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field
3-5 years of experience in analytics engineering, data engineering, or data science
Experience building, testing, and maintaining production-grade data transformation workflows and semantic models
Experience with dimensional data modeling techniques, including Star Schema and Kimball methodology
Experience using dbt or semantic tools to define, document, and communicate metric logic
Experience modeling business domain data, product usage metrics, and semi-structured JSON data for BI consumption
Software engineering mindset applied to data, including Git, automated testing, CI/CD, data modeling, and code documentation
Proficiency in advanced SQL, dbt, Snowflake, Python, Git, and modern BI tools such as Looker or Tableau
Experience with Google Analytics, marketing, advertising and social media platforms, CRM/Salesforce, and BI visualization tools
Knowledge of the modern data stack, including modeling, semantic layers, testing, documentation, governance, and BI integration
Knowledge of dimensional data modeling techniques, including Kimball methodology, Star Schema, and Snowflake Schema
Must be legally authorized to work in the United States without sponsorship now or in the future
Candidates must be based in one of the listed hiring states

Benefits

Comp & perks

Annual Bonus and Stock Option Program
Fully flexible workforce: remote work, Pittsburgh office, or a combination
100% paid employee health plan, including vision, dental, and supplemental coverage
Flexible Paid Time Off Policy
Work-from-home office stipend
12 weeks fully paid parental leave for all employees
Short-term disability for birthing parents
401(k) with employer match
Equal opportunity, inclusive work environment
Remote interviews