Lead Analytics Engineer

SavATree

Lead Analytics Engineer modernizing SavATree’s enterprise data and analytics capabilities. Building governed Snowflake, Databricks, and dbt products, internal tools, and AI-enabled workflow automations.

Posted 8/5/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $130,000 - $150,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced expertise in SQL, Snowflake, and dbt for data modeling, testing, and deployment, alongside practical Python skills for automation and integration. Proven ability to manage product lifecycles from stakeholder discovery to deployment, ensuring quality and user acceptance across complex operational environments.

Highest-signal resume keywords

SQL ExpertiseSnowflake ProficiencyDbt Model DevelopmentPython AutomationGitHub CI/CD Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringAnalytics EngineeringSoftware EngineeringData ProductsModelingTestingDocumentationLineageDeploymentData Validation

Soft Skills

CommunicationIndependent ExecutionStakeholder Engagement

Tools & Technologies

DatabricksReplitDynamics 365AzurePower PlatformSigma ComputingAI AgentsWorkflow Orchestration

Industry Keywords

CRM DataERP DataField-Service DataTransactional-System DataData ContractsData ObservabilityWarehouse Cost Optimization

Tech Stack

Tools & technologies

AzureERPPythonSQLUnity

About the role

Key responsibilities & impact

Discover user workflows and identify the decision or action behind requests
Define requirements, business rules, ownership, data dependencies, and acceptance criteria
Create product and technical roadmaps, sequence dependencies, and maintain the delivery backlog
Select system boundaries, architecture, data contracts, and user experiences
Build governed dbt models, analytical experiences, tests, and AI-assisted automations
Validate source data, business rules, user acceptance, and quality
Deploy, support, document, measure adoption, and continuously improve products
Retire redundant legacy workflows and dashboards after replacements are accepted
Meet with office managers, arborists, branch leaders, regional leaders, and executives to understand operational workflows
Build production-grade staging, intermediate, fact, dimension, and metric models in dbt
Model CRM, ERP, operational, historical, and third-party enterprise data
Implement freshness checks, documentation, lineage, observability, and GitHub CI/CD
Investigate discrepancies and reconcile results across operational systems, Snowflake, Databricks, dbt, Finance, and analytical products
Build scorecards, governed datasets, analytical workflows, alerts, and lightweight internal tools
Prototype and deliver focused internal experiences using Replit or comparable AI-enabled tools
Automate analytical, governance, operational, and engineering workflows using Python, SQL, orchestration tools, and AI agents
Inspect application entities, tables, columns, relationships, lifecycles, calculated fields, customizations, and business rules
Map approved business requirements to source entities and fields
Validate replicated application data for completeness, accuracy, timeliness, and analytical fitness
Identify and build AI-assisted internal tools, agents, and human-in-the-loop workflows
Evaluate emerging AI capabilities and translate promising ideas into controlled production experiments
Deliver roadmap milestones including governed models, reconciliation reporting, internal applications, AI workflows, and a certified core metric layer

Requirements

What you’ll need

7+ years of progressively responsible experience across data engineering, analytics engineering, software engineering, or data products
Advanced production experience with SQL, Snowflake, and dbt, including modeling, testing, documentation, lineage, and deployment
Databricks experience strongly valued
Demonstrated ownership of products from stakeholder discovery through roadmap, build, deployment, validation, and support
Practical Python experience for analysis, automation, integration, and lightweight application development
Ability to create internal tools and workflows without requiring a separate engineering team for every prototype
Strong Git and GitHub practices, including pull requests, reviews, automated testing, and CI/CD
Experience with CRM, ERP, field-service, billing, or comparable transactional-system data
Ability to communicate with frontline business users and senior technical stakeholders
Evidence of independent execution across ambiguous technical and organizational boundaries
Experience with Microsoft technologies such as Dynamics 365, Azure, Fabric, or Power Platform preferred
Hands-on Databricks experience, including lakehouse design, Delta tables, notebooks, jobs, or Unity Catalog preferred
Sigma Computing experience preferred
Experience with Replit or similar AI-enabled application platforms preferred
Experience with AI agents, retrieval-augmented generation, tool use, workflow orchestration, or agent evaluation preferred
Experience with semantic layers, metrics-as-code, data contracts, data observability, and warehouse cost optimization preferred
Experience in distributed, multi-location, field-service, or operationally complex businesses preferred

Benefits

Comp & perks

Annual bonus
Full-time, permanent, exempt employment
Remote work arrangement