Senior Analytics Engineer

Ryzlabs; Buenos Aires; Presencial; ARS 1.500.000 - 2.700.000; Jornada completa

Descripción de la vacante

Ryz Labs is seeking a hands-on Senior Data Engineer / Analytics Engineer to own and evolve data platforms, reporting layers, and AI-driven capabilities for a client at scale. You will report to the Head of Data & Analytics and work across finance, operations, merchandising, marketing, and product as a high-impact individual contributor.

You’ll move quickly in a startup/scale-up environment, taking ownership from problem definition through implementation while balancing speed and quality.

Formación

• 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 frameworks.
• 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 datasets.
• 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, etc.).
• Strong ownership mindset with the ability to operate independently.
• Comfortable in a fast-paced, ambiguous environment with shifting priorities.

Responsabilidades

• 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 environment.
• 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.

Conocimientos

SQL

Data modeling

BigQuery

GCP

Data pipelines

Data quality

BI dashboards

Finance analytics

Independent work

Cross-functional

Herramientas

Airbyte

Looker

Descripción del empleo

At Ryz Labs, we’re looking for a hands‑on Senior Data Engineer / Analytics Engineer to own and evolve one of our clients’ data platforms, reporting layer, and AI‑driven data capabilities. You’ll report directly to the Head of Data & Analytics and operate as a high‑impact individual contributor across finance, operations, merchandising, marketing, and product. This is a builder role, not a people manager role.

You’ll be expected to move quickly, work scrappily, and take ownership from problem definition through implementation. This role is ideal for someone who thrives in a startup or scale‑up environment, where speed, iteration, and pragmatism matter more than perfection.

What You’ll Do

• 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 environment.
• 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.

What We’re Looking For

• 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 frameworks.
• 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 datasets.
• 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, etc.).
• Strong ownership mindset with the ability to operate independently.
• Comfortable in a fast‑paced, ambiguous environment with shifting priorities.

Bonus Points

• Experience with ERP or inventory management systems (especially warehouse/3PL integrations).
• 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.