Full Stack Data Engineer

Mammoth Holdings LLC•Alcoa, TN

About The Position

We are seeking a Full Stack Data Engineer to join our Data & Analytics team. This role is for someone who is genuinely strong with databases and great at connecting things together: you will design and tune the SQL and Snowflake models at the heart of our platform, build and orchestrate the pipelines that move data between systems on AWS, and stitch applications, warehouse, and reporting layers into one coherent, reliable flow. What makes this role different is the domain. Our data comes off the tunnel point-of-sale and site controller systems such as DRB, membership and RFID plate-recognition data, wash counts, chemical and equipment telemetry, labor and payroll feeds, and marketing and CRM sources across a multi-brand, multi-site portfolio. You will be the person who turns that operational exhaust into trustworthy numbers the field and the executive team run on. Prior exposure to car wash operating platforms is a meaningful advantage; genuine curiosity about how a wash makes money is required. Just as important, you bring real analytics under your belt you can interrogate data, spot what matters, and turn it into Power BI dashboards and analyses the business trusts. You will also use modern AI tooling, including LLM-based workflows and MCP servers, to make the platform smarter and more automated. With roughly three years of professional experience, you will partner with senior engineers and business stakeholders to deliver production-grade data products from ingestion through insight.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field — or equivalent practical experience.
Approximately three years of professional experience in data engineering, analytics engineering, or a comparable technical role.
Deep database skills: expert SQL — confident writing, optimizing, and debugging complex queries — plus a solid grasp of relational design, indexing and clustering, and query performance.
Hands-on experience with Snowflake, or a comparable cloud data warehouse with willingness to go deep on Snowflake.
Experience building and maintaining dbt models, including testing and documentation.
Working experience with AWS and its core data services (S3, Lambda, Glue, IAM), and a track record of integrating applications, APIs, and data stores into orchestrated, dependable pipelines.
Strong analytics under your belt: proven ability to analyze data rigorously and communicate findings, with hands-on Power BI experience covering data models, DAX, and well-designed dashboards.
Working knowledge of modern AI tooling — LLM APIs, AI-assisted workflows, and familiarity with MCP (Model Context Protocol) servers or similar integration patterns.
Familiarity with Git and collaborative development workflows.
Solid problem-solving skills, with the ability to communicate technical results to non-technical audiences.

Nice To Haves

Hands-on experience working with car wash operating platforms and their data — DRB, Sonny’s, Washify, or comparable point-of-sale, site controller, and unlimited-membership systems.
Experience extracting, reconciling, or reporting on POS transaction and membership data in a multi-site environment.
Background in car wash, convenience retail, quick-service restaurant, fitness, or another high-volume, subscription- or membership-based multi-unit business.
Experience supporting data integration for acquisitions, conversions, or new site openings.
Experience with orchestration tools such as Airflow, Dagster, or dbt Cloud jobs.
Python for pipeline development, automation, and scripting.
Exposure to containerization (Docker) and infrastructure-as-code such as Terraform.
Experience building or integrating MCP servers, agents, or AI APIs into data workflows.
Familiarity with dimensional modeling and warehouse design best practices.
Experience administering or optimizing Snowflake, including warehouses, roles, and cost management.

Responsibilities

Design, build, and maintain ELT pipelines that ingest, clean, and transform data from multiple internal and external source systems into Snowflake.
Build and maintain reliable ingestion from car wash operating platforms — including DRB and comparable POS and site controller systems — handling site-level variation, historical restatements, and late-arriving transactions.
Develop well-structured, tested, and documented dbt models; write performant SQL for complex transformations across the warehouse.
Model core car wash domain concepts consistently across brands and sites — membership lifecycle, churn and retention, capture rate, average ticket, labor hours per wash, and site-level profitability — so a metric means the same thing everywhere it appears.
Own the scheduling, dependency management, and monitoring of engineering pipelines end to end — so jobs run in the right order, failures are caught early, and data lands fresh and on time for open-of-business reporting.
Connect things together: build the integrations that move data between source applications, APIs, the Snowflake warehouse, and downstream consumers across AWS, keeping the whole data flow coherent and reliable.
Support integration work tied to acquisitions and new site openings, including onboarding newly acquired locations and reconciling legacy platform data into the standard model.
Go beyond reporting — dig into the data to answer real business questions, validate assumptions, and surface trends and anomalies; bring sound analytical judgment to every dataset you touch.
Investigate operational questions that matter to the field, such as why membership conversion differs between comparable sites or how a promotion moved volume and retention.
Build and maintain Power BI dashboards and semantic models that stakeholders rely on daily, with clean data models, solid DAX, and clear visual design.
Use AI and LLM tooling — including MCP servers and AI-assisted development workflows — to automate data tasks, integrate AI capabilities into the platform, and prototype intelligent data services.
Write clean, well-documented, version-controlled code; participate in code reviews; and uphold data quality, testing, and monitoring standards across the stack.
Work closely with senior engineers, analysts, and business partners — including Operations and Finance — to scope problems, present findings, and iterate on solutions.