This is a role for 6 month period - Fixed term

Ryz Labs is looking for a Sr Data Analyst to join one of our client's team , an e-commerce platform.

As their Senior Data Analyst, you'll own the numbers the business actually decides on. Pricing, customer cohorts, payment systems, logistics, marketing channel performance — the analysis behind those calls comes from this seat. You'll work across the full width of a real operating e-commerce business: how visits become orders, how orders become delivered shipments, which customers come back, and what each of those is worth. Your job is not to produce dashboards. It is to produce decisions, and to be the person in the room who knows whether a number is trustworthy.

You'll report into Data & Analytics and work daily with Marketing, Operations, Finance, Customer Experience, and Engineering.

In a bootstrapped company, decisions get made on the numbers in front of us, quickly, and often without a second analyst to check the work. That makes accuracy the condition of this seat rather than a quality of it.

• Own in-depth analysis across the business: customer base and cohort behavior, pricing, payment systems, logistics and fulfillment performance, and marketing channel efficiency.

• Find the trends, patterns, and anomalies that matter, and separate them from noise — then say what you'd do about it.

• Partner with business-unit leaders, Finance, Operations, and executives to understand the decision behind the request, not just the data pull.

• Bring the recommendation, not only the output. An analysis that ends at "here's the chart" isn't finished.

• Build and maintain the data models that support recurring reporting and business requirements.

• Own the definition of the metrics you produce, and defend those definitions when different teams want the number to mean different things.

• Own the accuracy of at least one recurring business report end to end — its definition, its source, and whether anyone can trust it.

• Establish and hold data governance practices: naming, documentation, source of truth, and what happens when two dashboards disagree.

• Build interactive dashboards and reports that a non-technical leader can read without you in the room.

• Present insights to technical and non-technical stakeholders, including executives, and hold up under questioning.

• Write clearly. A short, direct summary of what changed and what it means beats a twelve-tab workbook.

• Deliver inconvenient findings early. A number that undercuts a plan is the most valuable thing this seat produces.

• Apply statistical techniques to test whether what we're seeing is real.

• Build and implement predictive models that support forecasting and planning decisions.

• Use advanced data mining techniques to surface insight nobody thought to ask for.

• Know the limits of your own models, and say them out loud when the output gets used.

• Work automation-first: use AI tools to draft queries, explore datasets, generate documentation, and accelerate analysis — then apply your own judgment before anything ships.

• Treat any analysis you run more than twice as a candidate for automation.

• Own the quality gate on machine-generated SQL and analysis. Verify the logic and the numbers against source before results leave your hands.

• Leave your reporting surface more automated at the end of each quarter than it was at the start.

• Numeric defensibility. Given a dataset and a business question, you can produce the figure, say where it came from, say how it's defined, and say what would make it wrong. We will test this directly with real data.

• An analysis that changed a decision. A specific piece of work where the business did something different because of what you found. What you looked at, what you found, what changed, and what it was worth.

• A caught-error story. A time you or a tool produced a confidently wrong number, how you caught it, and the checking habit you built as a result.

• An automation portfolio. Two or three recurring analyses or reports you personally automated, with what changed in hours saved or errors avoided. Rough honest numbers beat polished vague ones.

• Writing we can read. A short executive summary of an analysis, and one example where you delivered a finding that someone senior did not want to hear.

• Bachelor's or Master's degree in a quantitative field — Data Analytics, Statistics, Mathematics, Economics, Computer Science, or similar.

• 5+ years of professional experience, including 3+ years as a Data Analyst, preferably in e-commerce or a comparable high-volume, transactional business.

• Strong proficiency in SQL and Python for querying, analysis, statistical work, and visualization.

• Experience working with large-scale datasets and conducting real statistical analysis, not just aggregation.

• Working knowledge of data governance: source of truth, documentation, metric definition, and how to keep two reports from disagreeing.

• Strong prioritization and project management — you can carry several requests without dropping the one that matters most.

• Comfortable working independently in a fast-moving, lean environment where nobody is coming to scope the work for you.

• Experience with a cloud data warehouse (Redshift, Snowflake, BigQuery) and dbt or an equivalent transformation layer.

• E-commerce and marketing analytics depth: channel attribution, ROAS, cohort and retention analysis, LTV, promotional lift.

• Exposure to apparel, distribution, 3PL, or another high-volume, product-based operation.

• Financial statement literacy — you can read a P&L and ask a useful question about it.

• Hands-on with AI and automation tooling (Claude, agentic SQL workflows, Apps Script, or similar) beyond occasional prompting.

• Experience at a bootstrapped, profitable, or otherwise resource-disciplined company.

• Experience mentoring junior analysts or reviewing others' analytical work.