RYZ Labs - Senior Data / Analytics Engineer
Requirements
• 5–10+ years of experience in data engineering, analytics engineering, or advanced • analytics roles. • Strong experience with GCP and BigQuery, including materialized views, scheduled • queries, and large-scale SQL optimization. • Experience with modern data ingestion tools—Airbyte (Cloud or OSS) strongly preferred; • comfort managing connectors, debugging sync failures, and building validation • Strong proficiency in SQL and data modeling, with comfort using AI tools (e.g., Claude • Code) to accelerate development. You should be fluent in CTEs, window functions, • UNION ALL patterns, date-spine techniques, and anti-join logic. • Proven experience supporting financial reporting and working closely with finance • teams—P&L reconciliation, COGS analysis, revenue waterfalls, and unit-economics • Experience building dashboards and reporting in modern BI tools. • Familiarity with AI workflows and building structured datasets for LLM-powered agents. • Experience working across multiple business domains (ops, marketing, finance, product, • Strong ownership mindset with the ability to operate independently. • Comfortable in a fast-paced, ambiguous environment with shifting priorities. • Experience with ERP or inventory management systems (especially warehouse/3PL • Experience in ecommerce, recommerce, logistics, or marketplace • businesses—especially Shopify-based platforms. • Familiarity with multi-touch attribution, post-purchase surveys (e.g., Fairing/PPS), or • event-level GA4 data. • Experience with customer cohort analysis, retention modeling, or LTV forecasting. • Exposure to pricing, inventory aging, or supply chain/fulfillment data systems. • Experience with Klaviyo, Attentive, or similar lifecycle marketing data integrations. • Familiarity with demographic enrichment tools or custom API connector development.
Responsibilities
• Own and evolve data architecture across ingestion, transformation, and • reporting layers, with a centralized cloud data warehouse. • Build and maintain scalable data pipelines across a variety of internal and external data • sources, ensuring reliability, completeness, and accuracy. • Develop robust validation frameworks to monitor data quality and quickly identify issues. • Write and optimize complex SQL to power analytics, reporting, and business • decision-making. • Design and maintain data models that support financial reporting, operational analytics, • and merchandising insights. • Partner closely with Finance to ensure accurate, reconcilable reporting across revenue, • costs, and unit economics. • Build and maintain dashboards and reporting used by leadership to drive decisions • across the company. • Identify and resolve data issues quickly, balancing speed and accuracy in a fast-moving • Support integrations between core business systems and ensure clean, consistent data • across platforms. • Explore and implement AI-driven workflows that enhance data accessibility and • decision-making. • Automate manual reporting processes and improve operational efficiency across teams. • Act as a cross-functional partner, translating ambiguous business questions into clear, • actionable insights.
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