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.

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.

Senior Data – Analytics Engineer

Key skills