Note: The job is a remote job and is open to candidates in USA. Luxury Presence is building an AI growth platform for the real estate industry. The company is seeking a Senior Analytics Engineer to build and scale trusted analytical datasets, data pipelines, semantic layers, and measurement frameworks supporting cross-functional decision-making and AI-powered tools.
Responsibilities
- 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
- 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
- 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
- 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
Skills
- 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
- 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)
Company Overview