luxurypresence - Staff Analytics Engineer - CANADA (Remote)
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Requirements
• You already build with AI daily. You use Claude Code as a core part of your workflow, not as a novelty • You have strong opinions on how AI can enhance analytics workflows, data modeling, and decision-making • You think in systems. You connect technical decisions to customer outcomes and long-term business value • You communicate clearly and directly. You can explain complex tradeoffs to product, design, and executive stakeholders • You're energized by ambiguity and speed. You thrive in a fast-growing company where the roadmap evolves and ownership is real • You like to have fun at work. We take our craft seriously, but we don't take ourselves too seriously. We celebrate wins, crack jokes, and genuinely enjoy building together • 8+ years in analytics engineering, data engineering, or related roles, including time operating at a large scale or leading cross-functional initiatives. • Deep proficiency with dbt, including building and maintaining large-scale transformation layers and reusable models. • Strong SQL skills and a background working with Snowflake or similar cloud data warehouses. • Designing and operating scalable semantic layers to provide consistent, reliable metric definitions across the organization. • Expertise in designing modular, scalable data architectures (e.g., Kimball or OBT) that balance query performance with ease of use for downstream analysts. • A history of building data quality frameworks, testing strategies, and monitoring for analytics datasets. • Building data products or internal tools while partnering with product and business teams to define metrics and drive decisions. • Expert-level grasp of software design principles and a deep understanding of multi-tenant platform architectures. • Proven track record leading high-impact analytics initiatives from concept through delivery. • Frontend: Sigma, Hex • Backend: dbt, SQL, Python • Data: Snowflake • Infrastructure: Airflow • The real estate industry is in the midst of a seismic shift, and the future belongs to those who break new ground. As one of the fastest-growing companies in the proptech and marketing sectors, Luxury Presence challenges the status quo of what technology can do for real estate agents, leaders, and brokerages. • We're a team of agile and tenacious innovators working collaboratively to drive the industry forward. Together, we build game-changing products that empower modern real estate entrepreneurs to dominate their markets. From award-winning web design to agile SEO solutions to cutting-edge AI tools, we deliver tech that anticipates market shifts and keeps our clients ahead of their competition. • Founded in 2016 by Stanford Business School alum Malte Kramer, Luxury Presence has grown to a global team ranked on the Inc. 5000 fastest-growing companies list three years in a row. We're backed by world-class investors, including Bessemer Venture Partners, NextEquity Partners, Toba Capital, and Switch Ventures, and have raised $89 million to date. • More than 18,000 real estate businesses rely on our platform, including 30% of the Wall Street Journal RealTrends top agents and teams. Additionally, many of the industry's most powerful brokerages rely on Luxury Presence as a trusted business partner. • Every year since 2020, Luxury Presence has ranked on BuiltIn's Best Place to Work lists. HousingWire named our founder and CEO a 2024 Tech Trendsetter, we've received several Tech100 Awards, and we just scored an Inman Innovation Award for Best AI-Powered Platform.
Responsibilities
• Own and evolve a tool-agnostic analytics layer powering business decisions. Design and maintain scalable, well-modeled datasets using dbt, Snowflake, and a semantic layer—transforming raw data into trusted, high-quality metrics used across Product, GTM, Finance, and Operations. • Partner deeply with analysts and business stakeholders. Collaborate with Data Analysts, Product, and business leaders to define metrics, enable self-service analytics, and deliver insights that directly impact decision-making and product development. • Drive data quality, consistency, and trust at scale. Implement robust testing, monitoring, and validation frameworks to ensure data accuracy and reliability, while standardizing metric definitions across the organization. • Build data products and AI-powered analytics systems. Develop systems such as AI-driven support ticket classification and contribute to building an AI agent that can act as a Data Analyst—leveraging the semantic layer to answer business questions and generate insights.
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