About this role.
This senior analytics engineering role owns the analytical foundation supporting product, GTM, finance, people, and operations decisions at a SaaS company. The position centers on dbt, Snowflake, SQL, Python, Airflow, API ingestion, reconciliation, testing, and governed semantic layers. It requires close partnership with engineering, product, marketing, RevOps, and finance to establish trusted metrics and self-service analytics. The role also supports AI-ready data infrastructure through Snowflake Cortex semantic views and measurement frameworks for AI-powered initiatives. Success depends on independently resolving ambiguous data-quality and cross-system consistency problems while improving reliability and time-to-insight.
Role DNA
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Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
5/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Team,
I am excited to apply for the Senior Analytics Engineer role at Luxury Presence. My background building reliable analytics platforms with SQL, dbt, Snowflake, Python, and automated data pipelines aligns closely with your need for a trusted, scalable data foundation.
I have experience translating complex product, marketing, financial, and operational data into governed models, consistent metrics, and self-service insights for cross-functional stakeholders. I would welcome the opportunity to bring strong ownership, data-quality discipline, and semantic-layer expertise to help Luxury Presence power better decisions and AI-enabled analytics.
Thank you for your consideration.
Sample interview questions
I would organize models into clear staging, intermediate, and marts layers, with source freshness checks and standardized naming. I would create conformed dimensions and carefully documented fact models for core entities such as customers, subscriptions, opportunities, and product events. I would also add tests, exposures, lineage documentation, and metric definitions so downstream dashboards and semantic views use governed logic.
I would first define the business grain and canonical identifiers for each entity, then profile source-system coverage, latency, and known mapping gaps. The reconciliation models would join normalized records, apply deterministic and documented deduplication rules, and classify discrepancies such as missing records, status mismatches, amount variances, and timing differences. I would publish exception tables with ownership, severity, and remediation status, supported by automated alerts and recurring stakeholder review.
I would implement source freshness checks, schema and business-rule tests, volume and distribution anomaly monitoring, and pipeline-level service-level objectives. CI/CD would run linting, unit or integration tests, selective dbt builds, and code review before deployment. For incidents, I would maintain clear runbooks, trace lineage to identify root causes, communicate impact promptly, and track corrective actions through completion.
I would begin with high-value, well-defined business domains and model governed entities, measures, dimensions, joins, and approved synonyms. Each metric would have a clear owner, definition, grain, filtering behavior, and validation queries to prevent inconsistent answers. I would enforce role-based access controls and test AI outputs against known scenarios before release, then monitor usage and feedback to refine the semantic views safely.
I would define the decision, target population, primary success metric, guardrails, and attribution window before launch. Where feasible, I would use a randomized control or holdout group and measure incremental changes in qualified engagement, conversion, retention, or revenue rather than relying only on activity volume. I would account for channel overlap, sample-size requirements, and data-quality checks, then present results with confidence intervals and actionable recommendations.
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 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 Role
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, 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.
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.
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.
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.
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 analytics (headcount, attrition, compensation benchmarking).
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 agents can use to answer stakeholder questions — and actively maintain the semantic views that power those agents.
Proactively identify and quantify data discrepancies across systems and drive them to resolution with operational teams.
Design measurement frameworks for new initiatives — defining what to track, how to measure impact, and what “success” means before launch.
Compensation
Additional Information
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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