Lead Analytics Engineer

Description Position Title: Lead Analytics Engineer Status: Full-Time / Permanent / Exempt Location: Remote (must be located in or willing to work scheduled aligned with CST or EST) Salary: $130,000-$150,000 Per Year + Annual Bonus Position Summary SavATree is modernizing its enterprise data and analytics capabilities. We are seeking a senior, hands-on engineer who can lead business-facing discovery while personally delivering governed data products, analytical experiences, and workflow automations. This is not a reporting-only, project-management-only, or architecture-only role. The successful candidate will work directly with operators and leaders, translate ambiguous needs into an executable roadmap, inspect operational-system data and business logic, build governed models in Snowflake, Databricks, and dbt, and use modern AI-enabled tools to deliver useful analytical products and workflow automations. The role will help determine which operational capabilities belong in enterprise applications, which should become governed data products, which require a lightweight purpose-built experience, and which should be retired. What this person owns Discover — observe users, understand workflows, and identify the decision or action behind a request. Define — document the user, requirement, business rules, owner, data dependencies, and acceptance criteria. Plan — create the product and technical roadmap, sequence dependencies, and maintain the delivery backlog. Design — choose the correct system boundary, architecture, data contract, and user experience. Build — write the SQL and dbt models, configure analytical experiences, create tests, and implement useful AI-assisted automations. Validate — reconcile source data, test business rules, obtain user acceptance, and monitor quality. Operate — deploy, support, document, measure adoption, and continuously improve the product. Retire — remove redundant legacy workflows and dashboards after replacements are accepted. Core responsibilities Business discovery and product leadership Meet directly with office managers, arborists, branch leaders, regional leaders, and functional executives to understand how work is actually performed. Turn requests such as “rebuild this dashboard” into clear requirements describing the user, decision, action, outcome, owner, rules, and acceptance criteria. Create and maintain a capability-level roadmap spanning enterprise applications, Snowflake, Databricks, dbt, Sigma, Replit, Excel, AI experiences, and legacy retirement. Surface missing business ownership and conflicting definitions rather than silently inventing requirements. Demo working increments, gather feedback, and drive business-owner acceptance. Snowflake, Databricks, DBT, and Analytics Engineering Design and build production-grade staging, intermediate, fact, dimension, and metric models in dbt. Model Fivetran-delivered CRM, ERP, and operational data alongside historical and third-party enterprise sources. Own downstream transformation, semantics, reconciliation, and quality rather than building custom ingestion connectors where Fivetran already provides replication. Implement tests, source freshness checks, documentation, lineage, observability, and CI/CD through GitHub. Investigate discrepancies and reconcile results across operational systems, Snowflake, Databricks, dbt, Finance, and downstream analytical products. Develop reusable governed data products instead of embedding critical logic in individual dashboards. Data products and workflow automation Build decision-ready scorecards, governed datasets, analytical workflows, alerts, and lightweight internal tools. Use Sigma effectively where it remains the right delivery surface, while keeping business logic portable in Snowflake, Databricks, and dbt. Use Replit or comparable AI-enabled application tools to prototype or deliver focused internal experiences when standard analytical tools are insufficient. Automate repetitive analytical and governance workflows using Python, SQL, orchestration tools, AI agents, and governed enterprise data. Own products from prototype through validation, documentation, adoption measurement, support, and retirement. Enterprise application data Inspect application entities, tables, columns, relationships, status lifecycles, calculated fields, customizations, and business rules. Partner with functional and technical workstreams to map approved business requirements to source entities and fields. Require usable source-to-target mappings and history behavior before downstream implementation begins. Validate that replicated application data is complete, accurate, timely, and fit for analytical use. Keep record-level operational work in enterprise applications whenever practical; use the data platform for cross-branch, historical, cross-system, and enterprise measurement. AI agents and workflow automation Identify high-value opportunities to automate repetitive analytical, operational, and engineering workflows. Design and build AI-assisted internal tools, agents, and human-in-the-loop workflows grounded in governed enterprise data. Use AI coding and application-development tools to increase delivery speed without compromising security, testing, maintainability, or business ownership. Evaluate emerging AI capabilities pragmatically and translate promising ideas into controlled production experiments. Required qualifications 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 is strongly valued. Demonstrated ownership of a product from stakeholder discovery through roadmap, build, deployment, validation, and support. Practical Python experience for analysis, automation, integration, and lightweight application development. Ability to create useful 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 working with data from a CRM, ERP, field-service, billing, or comparable transactional system. Ability to communicate clearly with both frontline business users and senior technical stakeholders. Evidence of independent execution across ambiguous technical and organizational boundaries. Preferred qualifications Experience with Microsoft technologies such as Dynamics 365, Azure, Fabric, or Power Platform. Hands-on Databricks experience, including lakehouse design, Delta tables, notebooks, jobs, or Unity Catalog. Sigma Computing experience, including workbook design, governed data models, usage analysis, and migration or rationalization. Experience building and deploying internal applications with Replit or similar AI-enabled application platforms. Hands-on experience with AI agents, retrieval-augmented generation, tool use, workflow orchestration, or agent evaluation. Experience with semantic layers, metrics-as-code, data contracts, data observability, and warehouse cost optimization. Experience in a distributed, multi-location, field-service, or operationally complex business. What this role is not A dashboard factory or ticket-taking report developer. A project coordinator who does not write production code. A software engineer who is merely willing to learn Snowflake, Databricks, and dbt. A data engineer who works only from fully specified requirements. A substitute owner for undefined business policy or missing source-system decisions. Measures of success First 90 days Map the current operational systems, Snowflake, Databricks, dbt, Sigma, Replit, and GitHub landscape. Establish the capability inventory, ownership model, and requirements-to-data traceability approach. Publish a prioritized roadmap and identify the highest-risk application and data dependencies. Ship at least one meaningful end-to-end product increment. First six months Implement governed dbt models for priority business domains and validate them against source behavior. Deliver reconciliation reporting and retire or prepare to retire selected legacy workflows. Establish repeatable GitHub-based development, testing, deployment, and documentation practices. Launch a useful internal application or AI-enabled workflow with measurable adoption. First year Deliver a certified core metric layer independent of the presentation platform. Provide traceability from priority business requirements through source applications and the governed data platform. Reduce duplicate business logic across analytical workbooks and custom applications.