Lead Analytics Engineer

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.