## DescriptionLuxury 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 that has hit $100M in annual recurring revenue. More than 90,000 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.
The RoleWe're looking for a Senior Analytics Engineer to build and scale the analytical foundation that powers decision-making across Go-to-Market, Product, Finance, People, and Operations teams.
You will sit at the intersection of data engineering and analytics: transforming raw product, marketing, financial, and operational data into clean, well-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, billing operations, AI-powered outreach, and semantic layers that let AI agents answer stakeholder questions autonomously.
This is a highly cross-functional role — you'll partner closely with Product Management, Marketing, RevOps, Finance, People Ops, and Engineering to ensure our analytics stack is robust, scalable, and aligned with the business.
ResponsibilitiesBuild & 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.-Build and maintain custom Python/Airflow pipelines to ingest data from third-party APIs into Snowflake.-Design and operate cross-system reconciliation models that compare data across source systems to surface discrepancies and protect revenue.




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.-Investigate and document data incidents end-to-end — from root cause analysis through remediation tracking and stakeholder communication.
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.
Build AI-Ready Data Infrastructure-Design and maintain Snowflake Cortex semantic views that serve as the governed data interface for AI agents and LLM-powered tools.-Partner with AI/product teams to scope, build, and validate the semantic layer definitions that power internal AI assistants.-Build measurement frameworks for AI-powered initiatives — including experiment design and attribution modeling.
QualificationsMust 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 contemporary data modeling best practices.-Proficiency in Python for pipeline development, API integrations,



and automation.-Experience modeling Salesforce data — opportunities, contracts, subscriptions, cases, and field history.-Proven experience building custom ELT pipelines that ingest data from third-party APIs into a cloud data warehouse.-Experience designing cross-system reconciliation models — joining, deduplicating, and comparing data across multiple source systems to surface discrepancies.-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.-Experience designing and maintaining semantic layers that serve as governed data interfaces (dbt Semantic Layer, Snowflake Cortex, or similar).-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:-Experience building and maintaining Airflow DAGs and orchestrating multi-source API ingestion pipelines.-Strong foundation in statistics and experiment design — A/B testing, significance testing, and measuring incremental impact.-Experience with predictive modeling fundamentals — classification, feature selection, and model evaluation.-Familiarity with financial SaaS metrics and billing operations (ARR/MRR/NRR, subscription reconciliation, revenue recognition).-Experience with people

📌 Senior Analytics Engineer (Winnipeg)
🏢 Devconnectplatform
📍 Winnipeg