Senior Data Analyst

Zendesk

Senior Data Analyst converting raw data into actionable insights for Zendesk’s engineering and product teams. Working cross-functionally to develop metrics, reports, and dashboards that facilitate data-driven decision making.

Posted 7/30/2026full-timeMadison • Texas, Wisconsin • 🇺🇸 United StatesSenior💰 $151,000 - $227,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Proficiency in SQL and dbt for data transformation, along with experience in data visualization tools like Tableau and Looker. Strong analytical skills to derive insights from data, coupled with the ability to communicate findings effectively to both technical and non-technical stakeholders.

Highest-signal resume keywords

SQL ProficiencyDbt ExperienceData Visualization ToolsCloud Data Warehouse ExperienceData Quality Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLDbtData VisualizationPythonData ModelingData AnalysisAPIsCloud Object StorageData Quality TestsAI Tooling

Soft Skills

Analytical MindsetDetail-OrientedCross-Functional CollaborationEffective CommunicationSelf-Starter

Tools & Technologies

TableauLookerSnowflakeBigQueryRedshiftDatabricksMonte CarloGit/GitHubCI/CDAI Agents

Industry Keywords

Engineering Productivity MetricsDORA MetricsData-Driven Decision-MakingData QualityQuantitative Analysis

Tech Stack

Tools & technologies

Amazon RedshiftAWSBigQueryCloudGoogle Cloud PlatformOpen SourcePythonSQLTableau

About the role

Key responsibilities & impact

Develop SQL queries and dbt models to transform engineering and operational data into trusted, analysis-ready data models
Build and maintain self-service dashboards and reports that put engineering productivity, AI adoption, reliability, and cost metrics in front of engineers and leaders up to the VP+ level
Define and standardize metrics across engineering teams — owning the semantics of what a metric means (funnel stages, eligibility, DORA definitions like change-failure-rate and cycle time) so comparisons stay valid
Measure platform adoption, AI tool usage, and ROI across engineering, and communicate findings through a thoughtful combination of quantitative analysis and qualitative storytelling
Proactively conduct analyses and investigations that identify insights into underlying engineering and business matters — digging into data anomalies and asking "why" until you understand root causes
Write clear documentation and enablement material so stakeholders can self-serve and trust the data
Build relationships and collaborate with internal engineering, product, and enterprise data and analytics teams — reviewing peers' work and aligning on shared definitions
Implement data quality tests, monitoring, and validation (e.g. dbt tests, Monte Carlo) to ensure accuracy and prevent invalid metric comparisons
Help integrate data from APIs and third-party tools into Snowflake for analytics and AI enrichment

Requirements

What you’ll need

3+ years of experience in the analytics or data space, delivering analyses and metrics that drive decisions
Proven proficiency in SQL — comfortable with complex queries and transforming data into analysis-ready models
Hands-on experience with dbt (or a strong willingness to ramp quickly)
Experience with data visualization / BI or dashboarding tools (e.g. Tableau, Looker)
Experience with a cloud data warehouse (e.g. Snowflake, BigQuery, Redshift, Databricks)
Internally motivated, self-starter with an analytical and curious mindset — you find insights and show the value of data-driven decision-making
Ability to work cross-functionally and communicate technical concepts to both technical and non-technical audiences, up to the executive level
Detail-oriented with a passion for data quality, problem solving, and reliable, well-defined metrics
Proficiency in Python and familiarity with data modeling, forecasting, and data analysis techniques.
Experience developing and deploying open source BI solutions
Familiarity with software engineering best practices — Git/GitHub PR workflows, code review, CI/CD, and testing
Background working with large datasets, data APIs, and cloud object storage (AWS/GCP), plus data quality monitoring tools (Monte Carlo, dbt tests)
Fluency with modern AI tooling (Claude, GPT/Codex, MCP servers, AI agents) and experience embedding AI-assisted workflows into analytics work.
Knowledge of engineering productivity metrics — DORA metrics, PR review cycles, deployment frequency, incident management KPIs.

Benefits

Comp & perks

Bonus
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