Job Description

Role Description

Investigate, reconcile, and build the data infrastructure for a subscription ecommerce company (Mystery Tackle Box / Catch Co) that is rebuilding its growth marketing function from scratch. The data environment spans multiple platforms with known quality issues, conflicting sources, and no unified reporting layer. This role is equal parts detective work and system building.

Core Responsibilities

• Investigate and reconcile data across Recharge (subscription management), Shopify (ecommerce), Klaviyo (email/SMS), Source Medium (analytics reporting), Snowflake (warehouse), and paid ad platforms (Meta, Google).
• Surface data quality problems proactively. The current environment has known discrepancies across platforms that require investigation and resolution.
• Build cohort level retention and LTV analysis from raw transactional data, segmented by acquisition channel, subscriber tier, and plan type.
• Develop and maintain a channel level payback model connecting acquisition costs to downstream subscription revenue, churn, and lifetime value.
• Support financial model updates by providing clean, segmented inputs (tier weighted COGS, gift vs paid subscriber separation, adjusted churn rates).
• Validate and extend the existing Snowflake ETL pipeline from Recharge, backfill historical data where gaps exist, and work toward a single source of truth.
• Produce channel level performance reporting for recurring business reviews and leadership reporting packages.
• Support A/B test design and statistical analysis for landing pages, email flows, and ad creative.

Qualifications

• You're a data detective, not a dashboard reader. When numbers don't add up across platforms, you dig until you find out why—and you document what you found so it doesn't happen again.
• You're comfortable with ambiguity. Messy data, missing join keys, and conflicting sources don't stop you—they're the job, and you know how to work through them systematically.
• You build for permanence. You don't just answer the question in front of you; you build the model, pipeline, or framework that answers the next ten questions too.
• You surface problems before being asked. If something looks off, you flag it—with context, a hypothesis, and ideally a fix.
• You translate data into decisions. Your output isn't a spreadsheet; it's a clear narrative that non-technical stakeholders can act on.
• You use AI tools as a force multiplier. You reach for LLMs, code interpreters, and AI assisted analysis when they can accelerate your work. Not as a crutch, but as a way to move faster through data cleaning, hypothesis generation, and exploratory analysis.

Requirements

• SQL proficiency (writing queries against warehouse data, not just using a GUI).
• Excel/Google Sheets at an advanced level (pivot tables, lookups, data modeling, large dataset manipulation).
• Demonstrated comfort using AI/LLM tools (Claude, ChatGPT, Copilot, or similar) for data exploration, code generation, and analytical workflows.
• Demonstrated ability to reconcile conflicting data sources and determine which source to trust.
• Experience building cohort retention curves and LTV models from raw transactional data.
• Experience working with ecommerce or subscription data (Shopify, Recharge, Stripe, or equivalents).
• Comfortable working with messy, incomplete, or conflicting data where clean join keys do not exist.
• Self directed problem solver who surfaces issues before being asked. This is not a ticket driven reporting role.
• Strong written communication in English for presenting findings to non-technical stakeholders.

Preferred Skills

• Experience with Snowflake or similar data warehouses, including pipeline validation and data engineering.
• Experience with ad platform data (Meta Ads Manager, Google Ads) and understanding of attribution models.
• Experience with Klaviyo or similar email/SMS marketing platforms.
• Experience building AI-assisted analytical workflows (e.g., using LLMs for data reconciliation, anomaly detection, or automated reporting).
• Experience with Source Medium or similar analytics reporting tools.
• Python or R for data manipulation and automation.
• Financial modeling experience (LTV, payback period, contribution margin).
• Experience in a subscription box or D2C subscription business.

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