Senior Analytics Engineer

Luxury Presence

Senior Analytics Engineer building trusted, AI-ready analytics infrastructure for Luxury Presence’s real estate growth platform. Owning dbt, Snowflake, Python pipelines, semantic layers, and cross-system data quality.

Posted 8/10/2026full-timeRemote • 🇨🇦 CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining data pipelines using Python and Airflow, while ensuring data quality and reliability in a Snowflake environment. Proficient in modern data modeling practices and capable of designing cross-system reconciliation models to enhance analytics and drive business decisions.

Highest-signal resume keywords

SQL ExpertiseDbt ProficiencyPython DevelopmentSnowflake Data Warehouse ManagementCI/CD Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingAPI IntegrationCustom ELT Pipeline DevelopmentCross-System ReconciliationEvent-Based Data AnalysisAttribution ModelingSemantic Layer DesignAutomated TestingRoot-Cause AnalysisData Incident Documentation

Soft Skills

Stakeholder CommunicationCollaborationProblem-SolvingData Literacy PromotionOwnership of Ambiguous Problems

Tools & Technologies

AirflowPostHogMixpanelBigQueryRedshiftGit

Industry Keywords

SaaS EnvironmentAnalytics EngineeringData EngineeringFinancial SaaS MetricsBilling Operations

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudPythonSQL

About the role

Key responsibilities & impact

Build and own the dbt project, ensuring models are performant, well-tested, and documented
Design and maintain the Snowflake data warehouse and ingestion processes
Create core entities and datasets using modern data modeling best practices
Build and maintain custom Python/Airflow pipelines for third-party API ingestion into Snowflake
Design and operate cross-system reconciliation models to surface discrepancies and protect revenue
Implement testing and observability for analytics pipelines
Enforce CI/CD practices including automation, linting, tests, code review, and approvals
Standardize metric definitions across tools
Investigate and document data incidents from root-cause analysis through remediation and stakeholder communication
Act as data liaison between Engineering, GTM, and Finance
Enable stakeholder self-service access to trusted insights and promote data literacy
Design and maintain Snowflake Cortex semantic views for AI agents and LLM-powered tools
Partner with AI/product teams on semantic-layer definitions for internal AI assistants
Build measurement frameworks for AI initiatives, including experiment design and attribution modeling
Establish trusted analytics layers, improve data quality and reliability, reduce time-to-insight, quantify discrepancies, and define success metrics for new initiatives

Requirements

What you’ll need

5+ years of experience as an analytics engineer, data engineer, or 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 opportunities, contracts, subscriptions, cases, and field history data
Experience building custom ELT pipelines from third-party APIs into a cloud data warehouse
Experience designing cross-system reconciliation models across multiple source systems
Experience with event-based and product usage data such as PostHog or Mixpanel
Experience connecting paid advertising, campaign, and attribution data to product analytics
Experience designing and maintaining governed semantic layers such as dbt Semantic Layer or Snowflake Cortex
Comfort with large-scale data systems including Snowflake, BigQuery, or Redshift
Familiarity with CI/CD, Git-based workflows, and automated testing
Experience collaborating with engineers, analysts, and product managers
Demonstrated success using analytics to drive decisions in technical or product-focused environments
Comfort taking ownership of ambiguous problems and designing end-to-end solutions
Nice to have: Airflow DAGs, statistics and experiment design, predictive modeling, financial SaaS metrics and billing operations, and people analytics

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Luxury Presence Website LinkedIn All Job Openings 201 - 500 employees 🏠 Real Estate 📣 Marketing Real Estate
Marketing Luxury Presence is a company that specializes in providing real estate professionals with top-tier digital solutions. They offer award-winning real estate website designs and expert marketing solutions aimed at helping agents, teams, and brokerages grow their business and build their brand. Their platform includes tools for IDX home search, property websites, digital CMA, SEO services, content marketing, advertising, and social media management. Trusted by over 20 of the top 100 Wall Street Journal agents, Luxury Presence empowers real estate professionals to attract more business through beautiful and functional digital experiences. Senior Analytics Engineer Job not on LinkedIn 🔥 0 minutes ago 🇨🇦 Canada – Remote ⏰ Full Time 🟠 Senior 📊 Analytics Engineer Airflow Amazon Redshift BigQuery Cloud Python SQL Apply Now Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Build and own the dbt project, ensuring models are performant, well-tested, and documented
Design and maintain the Snowflake data warehouse and ingestion processes
Create core entities and datasets using modern data modeling best practices
Build and maintain custom Python/Airflow pipelines for third-party API ingestion into Snowflake
Design and operate cross-system reconciliation models to surface discrepancies and protect revenue
Implement testing and observability for analytics pipelines
Enforce CI/CD practices including automation, linting, tests, code review, and approvals
Standardize metric definitions across tools
Investigate and document data incidents from root-cause analysis through remediation and stakeholder communication
Act as data liaison between Engineering, GTM, and Finance
Enable stakeholder self-service access to trusted insights and promote data literacy
Design and maintain Snowflake Cortex semantic views for AI agents and LLM-powered tools
Partner with AI/product teams on semantic-layer definitions for internal AI assistants
Build measurement frameworks for AI initiatives, including experiment design and attribution modeling
Establish trusted analytics layers, improve data quality and reliability, reduce time-to-insight, quantify discrepancies, and define success metrics for new initiatives 🎯 Requirements
5+ years of experience as an analytics engineer, data engineer, or 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 opportunities, contracts, subscriptions, cases, and field history data
Experience building custom ELT pipelines from third-party APIs into a cloud data warehouse
Experience designing cross-system reconciliation models across multiple source systems
Experience with event-based and product usage data such as PostHog or Mixpanel
Experience connecting paid advertising, campaign, and attribution data to product analytics
Experience designing and maintaining governed semantic layers such as dbt Semantic Layer or Snowflake Cortex
Comfort with large-scale data systems including Snowflake, BigQuery, or Redshift
Familiarity with CI/CD, Git-based workflows, and automated testing
Experience collaborating with engineers, analysts, and product managers
Demonstrated success using analytics to drive decisions in technical or product-focused environments
Comfort taking ownership of ambiguous problems and designing end-to-end solutions
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