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.