Senior Data Analyst (Hybrid - Madison or Austin

Job Description About us We are looking for a Senior Data Analyst to support the Foundation Insights team. You will work cross-functionally to help drive analytics, enablement, and data-driven decision-making for our global engineering and product teams.

Foundation Insights at Zendesk owns operational data for Engineering and Product Development — measuring productivity, reliability, AI adoption, cost/OpEx, and infrastructure excellence.

In this role, you will turn raw engineering, product, and operational data into trusted metrics, self-service dashboards, and analyses that leaders act on.

You’ll own analytical domains end-to-end — from the SQL and dbt models that define a metric, to the interactive dashboards stakeholders read, to the definitions and documentation that keep everyone aligned.

You’ll work across a modern data stack — Snowflake, dbt, Airflow, Git Hub, and AI platforms like Claude, Codex and MCP servers — and your work will directly shape how Product Development measures and improves itself.

Location This hybrid role requires working from our Madison, WI or Austin, TX office at least two days per week or as determined by your manager.

• 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

• 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, Big Query, 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/Git Hub 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

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