The Juice Plus+ Company is a multimillion-dollar competitor in the health and wellness industry, using a unique combination of direct sales and e-commerce to market our plant-based nutritional products in pursuit of our mission to “Inspire Healthy Living Around the World.” We currently operate in 23 countries.

Overview

The Principal Data Engineer is the most senior hands-on engineer on the Global Enterprise Data team and the technical anchor of the enterprise data platform. This is a deliberately senior individual contributor role. It exists to carry the heaviest technical work end to end — design through delivery — with minimal oversight, and to raise the design quality of everything the team ships.

Our platform is small, highly interconnected, and directly relied upon for financial close, supply chain, and executive reporting. A change made in one layer has consequences several layers downstream. The person in this role is expected to see those consequences before the code is written, to choose the durable solution over the fast one, and to say so when a request would create a future problem.

This role does not manage people. There are no direct reports, no performance reviews, and no sprint administration attached to it. Influence is technical: through design, through review, and through the standards this person sets and upholds.

This is a role for someone who has already operated at this level, who is comfortable owning ambiguous problems from first conversation to production, and who can be trusted with the platform without supervision.

Responsibilities

Platform Engineering and Delivery

Own the design, build, and operation of the enterprise data platform’s most complex pipelines, models, and integrations — from raw ingestion through curated, analytics-ready layers.
Take full ownership of assigned initiatives end to end: requirements, design, build, test, deploy, and support. Deliver without needing direction on how to get there.
Build and optimize ELT/ETL workflows and transformations across Snowflake, Azure Data Factory, dbt, and Microsoft Fabric.
Design and maintain data models, warehouse structures, and integration frameworks that support fast, reliable analytics in Power BI.
Tune query, storage, and processing performance — partitioning, clustering, caching, warehouse sizing, and cost control — and eliminate bottlenecks before business users encounter them.
Build automated monitoring, alerting, logging, and reconciliation so that failures surface themselves and do not depend on manual checking or off-hours attention.

Design Authority and Technical Standards

Assess the downstream and upstream impact of every proposed change before work begins, and steer the team away from short-term fixes that create structural debt.
Review the designs and code of other engineers, providing direct, constructive technical feedback that raises the quality of what ships.
Define and enforce engineering standards — naming conventions, modelling patterns, source control, branching, CI/CD, environment management, and deployment practice.
Maintain architecture documentation, data lineage, and change-management records so that platform knowledge lives in the team, not in one person’s head.
Champion data quality, integrity, and consistency across the warehouse; build the controls that prove it rather than asserting it.
Partner with the Director, Global Enterprise Data on platform architecture and technical roadmap, and act as a credible second technical voice in those decisions.

Business Partnership

Work directly with Finance, Accounting, Supply Chain, Marketing, and Operations to understand the process behind the request — not just the request — and design data solutions that address the underlying need.
Identify manual, repetitive business processes that can be automated through the data platform, and propose and build those automations.
Act as the bridge between engineering and analytics, ensuring data is structured, documented, and accessible to analysts and self-service consumers.
Communicate technical constraints, trade-offs, and timelines clearly to non-technical stakeholders.

Artificial Intelligence and Automation

Contribute to the team’s AI roadmap — building the curated, well-governed data foundations that AI and agent-based tooling depend on.
Apply AI-assisted development tooling to accelerate delivery, and help establish the team’s practices for using it safely and effectively.
Evaluate and pilot emerging capabilities in the Microsoft Fabric and Snowflake ecosystems, and drive adoption where there is clear benefit.

Qualifications & Experience

The Principal Data Engineer must possess the following knowledge, skills, abilities and personal characteristics.

Required

8+ years of hands-on data engineering, data platform, or data architecture experience, including time at a senior or principal level.
Expert-level SQL, with demonstrable performance-tuning capability. Deep Snowflake experience strongly preferred.
Strong ELT/ETL development experience with Azure Data Factory, dbt, or comparable orchestration and transformation frameworks.
Proven track record designing and operating dimensional and analytical data models consumed by BI tools — Power BI experience strongly preferred.
Demonstrated ability to own complex, ambiguous work from first conversation through production with minimal direction.
Sound architectural judgement: able to evaluate trade-offs, anticipate downstream impact, and choose the durable solution.
Fluent with source control, CI/CD, and environment management for data workloads (Git, Azure DevOps, or equivalent).
Strong written and verbal communication skills, including the ability to explain technical decisions to business stakeholders and to disagree constructively.
Meticulous attention to detail; low tolerance for unreliable or unexplained data.
Available during U.S. Central business hours with substantial overlap with the Collierville, TN team.
College degree in computer science or a related field, or equivalent professional experience.

Preferred

Microsoft Fabric experience — Lakehouse, OneLake, Data Factory pipelines, or Fabric-based AI capabilities.
Practical experience applying AI or LLM-based tooling to data engineering or analytics work.
Exposure to ERP data and financial reconciliation processes (Sage, Dynamics, NetSuite, or similar).
Experience in direct sales, e-commerce, subscription commerce, or consumer goods.
Experience mentoring engineers or setting technical standards for a team, without formal management responsibility.

Disclaimer

This job description is only a summary of the typical functions of the job, not a comprehensive list of all possible job responsibilities, tasks and duties.

Job description