Product Data Analyst

Salsify's Product Data team exists to make R&D measurably better and to get the most value out of the data R&D produces. That splits into two jobs: showing whether our R&D teams and initiatives are succeeding, and turning R&D's data into decisions and outcomes across the business. We are hiring a Product Data Analyst to do both.

The work runs from discovery through delivery. You will sit with stakeholders in R&D and across the business to understand how data can support the outcomes they are after, work with Data Platform and data owners to get the right data in place, and then build the datasets, metrics, and reporting that drive action. You will shape the projects you work on: what the metric should be, how the dataset should be modeled, who acts on it, and what changes as a result.

We are looking for someone who treats data as a product and who can pragmatically match the solution to the value at stake, whether that is a fast answer to a question someone needs today or a dataset the company will depend on for years.

Responsibilities

• Measure the health of R&D. Define and maintain the KPIs and reporting that describe the health of the R&D function and whether its teams and initiatives are delivering. Work with PMs, engineering leaders, and UX to turn an initiative's stated goal into something that can actually be measured.
• Build the data products. Write the SQL, model the datasets, and stand up the reporting people act on. Decide which recurring requests point at something worth building once and maintaining. Partner with Data Platform when the source data does not exist yet or a pipeline needs to change.
• Drive impact with R&D's data across the business. Find where teams in Customer Success, Sales, Support, and Finance could drive better outcomes using R&D's data, then solution the data products and reporting that let us execute against those opportunities. Bring a recommendation rather than a menu of options.
• Own definitions and data quality. Make sure a metric means one thing across the company, is documented, and holds up when someone challenges the number.

What Success Looks Like

Ultimately: R&D and business leaders make decisions from data you built, without asking you to re-explain it or re-verify the number.

Along the way, you will know it is working when:

• Every significant R&D initiative has KPIs that leaders trust, and we can say whether the initiative worked
• Cross-functional partners are making decisions on data you delivered, and can point to what it changed
• Metric definitions are documented and stable, and disagreements about numbers are rare and quick to resolve
• The business has clear, concrete examples of value it drove with R&D's data

Qualifications

• 3+ years in an analyst or data role. Strong SQL. Comfortable with large, messy datasets and turning them into something reusable.
• Systems thinking. You can trace a metric from source system through transformation to the report, and you know what breaks downstream when a definition changes upstream.
• Stakeholder management. You can run a conversation with a PM, an engineer, or an executive and come out of it with a clear problem statement and agreement on what happens next.
• Product sense about data. You treat datasets and reporting as products with users, and you think about maintenance and adoption before you build.
• Pragmatism. You pick the option that gets the outcome, and you are direct about the tradeoff you made.
• Clear communication. You can explain what a number means, what it does not mean, and what someone should do about it.

Preferred Qualifications

• Snowflake and dbt
• Experience using AI to do data work: writing and debugging queries, exploring unfamiliar datasets, building transformations, drafting analysis
• Experience measuring the performance of a product or engineering organization
• Experience building data products used by people outside your own team, internally or customer-facing
• SaaS or B2B software background

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