• Help define and evolve enterprise data engineering blueprints, including data mesh, medallion architecture, and hybrid cloud data platforms. • Set strategic direction for data platforms, tools, and services (e.g., Snowflake, GCP BigQuery, dbt, Kafka, Airflow/Prefect) in alignment with future-state architecture and business priorities. • Architect and design highly scalable, resilient, cost optimal and secure data platforms. • Lead the design and implementation of next-generation data platforms, ensuring fault tolerance, high availability, and optimal performance for petabyte-scale data. • Establish and enforce organization-wide best practices for data pipeline development, CI/CD for data workflows, automated deployment playbooks, and robust rollback strategies. • Lead technology evaluation and adoption, proactively researching, evaluating, and championing the integration of cutting-edge data technologies, frameworks, and methodologies. • Define and scale enterprise knowledge management frameworks that ensure consistent documentation, discoverability, and reusability of data assets across domains. • Establish and govern standards for metadata management, data lineage, architectural diagrams, and runbooks. • Lead the design of federated governance models that empower domain-aligned teams to operate autonomously while conforming to centralized policies, frameworks and playbooks. • Collaborate with data governance, compliance, and security teams to operationalize policy-as-code frameworks for data retention, access control, and PII handling. • Advocate for and enable self-service knowledge discovery through tightly integrated cataloging tools (e.g., Alation, Collibra) and automated documentation generators. • Ensure robust documentation and versioning standards are embedded in CI/CD workflows for pipeline code, transformation logic, and schema changes. • Architect implementation of scalable, automated data quality frameworks that evaluate data at rest and in motion spanning completeness, timeliness, consistency, accuracy, and integrity. • Lead integration of data quality rules, metrics, and health indicators directly into orchestration layers (e.g., Prefect, Airflow) and transformation frameworks (e.g., dbt). • Evangelize a culture of data trust and transparency by integrating data quality insights into user-facing dashboards, alerts, and product health reports. • Identify and promote enterprise-wide data opportunities through thought leadership, white papers, reference architectures, and innovation labs. • Act as technical advisor to senior executives on data modernization, AI readiness, and platform consolidation strategies. • Serve as a strategic translator between complex business challenges and modern data architecture by leading domain-level and cross-domain data product strategy engagements. • Lead the design of enterprise-grade data products that align with OKRs, business transformation goals, and operational needs ensuring value realization across functional areas like supply chain, marketing, store ops, or customer satisfaction. • Architect and operationalize a unified enterprise-wide semantic layer, metrics store, and business logic abstraction that powers dashboards, self-service analytics, and machine-readable APIs. • Lead initiatives to unify KPIs, standardize metric definitions, and streamline business logic through reusable models. • Design composable data assets and feature stores that enable real-time and offline access patterns for ML models, AI agents, and decision orchestration systems. • Lead readiness initiatives for integrating data systems with LLM-powered agents and copilots, ensuring robust grounding data, latency optimization, and lineage tracking. • Drive innovation in analytics automation, including anomaly detection, agent-triggered insights. • Serve as champion for complex analytics transformations, ensuring technical feasibility, business value realization, and adoption. • Drive culture change around data stewardship and accountability by embedding governance responsibilities into platform tooling and engineering workflows. • Lead internal communities of practice, workshops, and code reviews to disseminate modern data practices. • Mentor senior engineers across data and analytics engineering, elevating technical acumen and architectural judgment. • Influence hiring and team design decisions, supporting the scaling of high-performing, and collaborative data teams. • Represent the organization in external forums (conferences, meetups, technical alliances) and establish credibility as an industry thought leader.

• Design and implement robust dimensional and relational data models that support analytical use cases across Product, Marketing, Operations, and Finance. • Build and maintain scalable dbt transformation pipelines, ensuring high data quality, performance, and cost-efficiency from raw ingestion to business-ready outputs. • Own the transformation and modeling of curated (Silver/Gold) datasets, ensuring clear contracts and traceability from raw to business-ready data. • Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products that support experimentation, A/B testing, and advanced analytics. • Implement data quality tests, monitoring, SLAs, and alerting to ensure reliability of critical analytical datasets. • Partner with Data Engineers to define and enforce data contracts, ensuring schema stability and minimizing downstream breakage. • Establish and evangelize analytics engineering best practices, including version control, code review, testing standards, and documentation. • Empower self-service analytics by building intuitive, well-documented data marts and semantic layers.

• Lead complex financial pipeline builds: Design and develop scalable data pipelines supporting banking processes, from scoping through delivery • Collaborate cross-functionally: Partner with Credit, Payment Operations, Tech, Security, Risk, and Finance teams to define data requirements • Build credit reporting infrastructure: Create reliable, performant operational data models that power internal and external reporting, audits, and money movements. • Design scalable data models: Apply modelling frameworks (one big table, entity tables, event streams, Kimball) tailored to credit reporting use cases • Optimize and monitor: Maintain pipeline health, optimize query performance, and implement data quality monitoring • Enable the team: Document solutions, establish best practices, and help upskill fellow Analytics Engineers and Analysts • Integrate cross-functional data: Work across domain boundaries to unify financial data into cohesive reporting structures

Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company on track to hit $100M in annual recurring revenue in the next six months. More than 87,000 real estate professionals, including over 30% of the WSJ Real Trends top 100 agents in the United States, use us to run and grow their business. We’re looking for a Senior Analytics Engineer to build and scale the analytical foundation that powers decision-making across Go-to-Market, Product, Finance and Operations teams. You will sit at the intersection of data engineering and analytics: transforming raw product, marketing, and financial data into clean, well-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, pricing, and AI product insights. This is a highly cross-functional role — you’ll partner closely with Product Management, Marketing, RevOps, Finance, and Engineering to ensure our analytics stack is robust, scalable, and aligned with the business. Responsibilities • Build & Own the Data Foundation ◦ Own and evolve our dbt project–ensuring models are performant, well-tested, and documented. ◦ Design and maintain the Snowflake data warehouse and ingestion processes. ◦ Use modern data modeling best practices to create core entities and datasets that account for complex business processes and logic. • Drive Data Quality & Automation ◦ Implement testing and observability for analytics pipelines ◦ Enforce CI/CD best practices, such as automation, linting, tests, code review and approvals. ◦ Standardize metric definitions and ensure they are consistently computed across tools. • Cross-Functional Collaboration ◦ Act as data liaison between Engineering, GTM, and Finance—ensuring consistent metric definitions and proper system instrumentation. ◦ Enable stakeholder self-service access to trusted insights. ◦ Drive data literacy: evangelize best practices in querying, dashboarding, and interpreting metrics; coach stakeholders toward self-serve. Qualifications Must Have: 5+ years of experience as an analytics engineer, data engineer, or a similar role in a SaaS environment. Deep expertise in SQL, dbt and modern data modeling best practices. Proven experience working with event-based and product usage data (e.g., Posthog, Mixpanel). Experience connecting marketing data (paid ads, campaigns, attribution) to product analytics–ideally having built end-to-end pipelines from ad platforms through to conversion and retention metrics. Comfortable with large-scale data systems (Snowflake, BigQuery, Redshift). Strong familiarity with CI/CD, Git-based workflows, and automated testing. Experience collaborating cross-functionally with engineers, analysts, and product managers. Demonstrated success using analytics to drive decisions in a technical or product-focused environment. Comfort taking ownership of ambiguous problems and designing end-to-end solutions. Nice to Have: Proficiency in Python for deeper analysis and automation. Experience building and maintaining Airflow DAGs. Experience with Spark and PySpark. What Success Looks Like Establish a trusted, well-modeled analytics layer that product managers, marketers, and leaders rely on daily. Improve data quality and reliability, with clear SLAs and observability around our most critical models. Drive down time-to-insight by enabling self-serve access to high-quality datasets and metrics. Extreme ownership over critical infrastructure and data models that directly impact product decisions and business growth. Partner with data engineers and analysts to build a semantic layer that AI can use to answer stakeholder questions. Join us in shaping the future of real estate 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. Luxury Presence is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.