Senior Data Analyst

Jobtailor; Town of Texas (WI); On-site; USD 80,000 - 120,000; Full time

Job summary

Jobtailor in Wisconsin seeks a data analytics professional to develop SQL queries and dbt models, transforming engineering and operational data into trusted analytics-ready datasets. You will build self-service dashboards and reports for engineers and leaders up to VP level, focusing on productivity, AI adoption, reliability, and cost metrics.

Define and standardize metrics across teams, measure platform adoption and ROI, and communicate findings with both quantitative analysis and qualitative

Qualifications

• 3+ years of experience in 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 (Tableau, Looker).
• Experience with a cloud data warehouse (Snowflake, BigQuery, Redshift, Databricks).
• Analytical, curious mindset with ability to communicate insights clearly to technical and non-technical audiences.

Responsibilities

• Develop SQL queries and dbt models to transform engineering and operational data into trusted analytics-ready data models
• Build and maintain self-service dashboards and reports that show engineering productivity, AI adoption, reliability, and cost metrics up to VP+ level
• Define and standardize metrics across engineering teams—own the semantics of metrics to keep comparisons valid
• Measure platform adoption, AI tool usage, and ROI across engineering, and communicate findings with quantitative and qualitative storytelling
• Proactively conduct analyses that identify insights into underlying engineering and business matters and investigate root causes
• Write clear documentation and enablement materials so stakeholders can self-serve and trust the data
• Collaborate with internal engineering, product, and enterprise data teams to review work and align on definitions
• Implement data quality tests, monitoring, and validation (dbt tests, Monte Carlo) to ensure accuracy
• Help integrate data from APIs and third-party tools into Snowflake for analytics and AI enrichment

Skills

SQL

dbt

Data visualization

Python

Data analysis

Cross-functional communication

Data quality

Open source BI

AI tooling

ETL/Data modeling

Tools

Tableau

Looker

Snowflake

BigQuery

Redshift

Databricks

Monte Carlo

GitHub

CI/CD

APIs

Job description

• 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

• 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.

Core Competencies

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.