The Role
Analyze large-scale transactional data using SQL and Python, produce defensible metrics, conduct statistical analysis, and turn findings into business decisions. Build and automate recurring reports, establish data governance and metric definitions, communicate insights to senior stakeholders, and prioritize multiple requests independently. The role also supports e-commerce and marketing analytics, including attribution, retention, LTV, promotional lift, and related business reporting.
Summary Generated by Built In
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.
Numeric Defensibility — The Condition of the Seat
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.
Key Responsibilities
Analysis That Drives Decisions
• 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.
Data Models, Metrics & Definitions
• 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.
Visualization & Communication
• 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.
Statistical & Predictive Work
• 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.
Working AI-First
• 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.
Requirements
Basic Qualifications
• 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.
Preferred Qualifications
• 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.
Skills Required
- Bachelor's or Master's degree in Data Analytics, Statistics, Mathematics, Economics, Computer Science, or a similar quantitative field
- 5+ years of professional experience, including 3+ years as a Data Analyst
- Strong proficiency in SQL
- Strong proficiency in Python for querying, analysis, statistical work, and visualization
- Experience working with large-scale datasets and conducting statistical analysis beyond aggregation
- Working knowledge of data governance, source-of-truth management, documentation, metric definitions, and report consistency
- Strong prioritization and project management skills
- Ability to work independently in a fast-moving, lean environment
- Ability to demonstrate numeric defensibility using real data
- Experience delivering analysis that changed a business decision
- Experience identifying incorrect analysis or reporting and establishing checking practices
- Portfolio of two or three personally automated recurring analyses or reports
- Strong written communication, including executive summaries and communicating unwelcome findings to senior stakeholders
- Experience with Redshift, Snowflake, BigQuery, or another cloud data warehouse
- Experience with dbt or an equivalent transformation layer
- E-commerce and marketing analytics experience, including attribution, ROAS, cohort and retention analysis, LTV, or promotional lift
- Exposure to apparel, distribution, 3PL, or another high-volume product-based operation
- Financial statement literacy, including the ability to read a P&L
- Hands-on experience with AI and automation tools such as Claude, agentic SQL workflows, or Apps Script
- Experience at a bootstrapped, profitable, or resource-disciplined company
- Experience mentoring junior analysts or reviewing analytical work
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Where startups soar! We're not just building startups - we're crafting the future. From ideation to execution, we provide the blueprint for startup success. We specialize in nurturing startups from the ground up, and supercharging existing ones with our top-tier technical talent solutions. Join us at Ryz Labs, where we turn promising ideas into thriving businesses. Let's shape the future of innovation together!
