Interested in this Data Engineer role at Metagenics?
Apply Now →Skills & Technologies
About This Role
⠀: About Metagenics
For over 43 years, Metagenics has had the mission to help people live happier, healthier lives through the life-changing power of nutrition.
We believe the way we do business is as important as the business we do; that a company in the nourishment business should nourish its people, too. So, we incorporated healthy, sustainable care into every dimension of our culture. Our diverse and expansive team are a prime example of the power of a people-first approach. We know first-hand, when an organization prioritizes internal growth and fosters empathy, its people come together to set an example of what the world can become.
Join us in being the world’s leading healthcare professional-grade VMS company!
⠀: The Role
The Data Engineer is responsible for building and scaling the data and AI foundations that power the business across North America. Based in the NAM region and reporting to the Global Data Engineering Manager, this role designs and operates modern data pipelines and AI-enabled solutions on Microsoft Azure and Microsoft Fabric, transforming data from ERP, commerce, marketing, and customer platforms into reliable, production-grade products.
This is a hands-on, delivery-focused role at the intersection of data engineering and applied AI. The role partners closely with commercial, marketing, and technology stakeholders across the NAM region to accelerate growth, improve the customer experience and unlock new capabilities through data and AI. Key Responsibilities
- Data pipelines & platform: Design, build, and maintain scalable, reliable data pipelines and lakehouse solutions on Microsoft Fabric, Synapse Analytics, and Azure. • AI engineering: Develop, integrate, and operationalize AI/ML and generative-AI solutions — from feature pipelines and model deployment to RAG, embeddings, and LLM-based applications — with a focus on production reliability. • Data modeling & quality: Build well-structured, governed data models; implement data quality, validation, lineage, and observability across the platform. • Integration: Ingest and unify data from ERP (Dynamics 365 Finance & Operations), commerce, marketing automation, and CRM systems to create a trusted view of customers, products, and orders across the NAM region. • Automation & MLOps: Apply CI/CD, infrastructure-as-code, orchestration, and MLOps practices to automate deployment, monitoring, and retraining. • Partnership: Work with business and technical stakeholders across the NAM region, and with global data and technology teams, to translate commercial needs into data and AI solutions that drive measurable outcomes. • Performance & cost: Optimize pipelines and workloads for performance, scalability, security, and cost efficiency in the cloud. • Governance & security: Ensure solutions meet data governance, privacy, and security standards across the NAM markets in which we operate. Basic Requirements
Experience / Training / Education · 3–5 years in data engineering, with demonstrated delivery of production data and/or AI solutions. · ERP (Dynamics 365 Finance & Operations): Working knowledge of D365 F&O (FinOps) — data entities, finance, sales, and supply chain modules, and how ERP data is extracted and modeled for analytics and reporting. · Microsoft Fabric: Hands-on experience delivering with Fabric — Lakehouse and Warehouse, Data Factory pipelines, Spark notebooks, semantic models, Mirroring, and Shortcuts. Awareness of capacity (CU / F-SKU) consumption and cost management is highly valued. · AI engineering: Practical experience designing, building, and deploying AI/ML solutions using Microsoft Foundry, Copilot Studio, large language models, embeddings, retrieval-augmented generation (RAG), and production workflow integrations. · Programming: Strong Python and SQL; comfortable with PySpark for large-scale data processing. · Data engineering fundamentals: ETL/ELT design, data modeling (dimensional & lakehouse/medallion), and building batch and streaming pipelines. · Ways of working: Comfortable with Agile delivery, with strong problem-solving skills and the ability to work independently across time zones and global teams. Compensation The current range for this role based out of Jersey City, NJ, is $130,000/yr. to $174,870/yr.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
This role may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance. Health and Wellness:
- Medical, dental, vision
- Annual employer HSA (Health Savings Account) funding for eligible employees who elect an HSA medical plan
- STD, LTD, Basic Life AD&D coverage
- 401k + employer matching
- Vacation and Sick Time
- 2 Volunteer Days off per year
- Emergency evacuation time off
- 11 paid company holidays
- 1 Floating holiday to celebrate your birthday or important religious/holiday to you
- Quarterly product allowance to use towards your favorite Metagenics products!
- Product discount
- Peer to peer recognition programs & more!
A minimum of three workdays per week are to be conducted at the Jersey City office (111 Town Square Pl, Jersey City, NJ 07310).
Learn more about how we help patient live happier and healthier lives here: https://www.metagenics.com/en-us/about-us
Metagenics, and its companies are committed to providing equal employment opportunity without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other protected status with respect to recruitment, hiring, promotion and other terms and conditions of employment. Metagenics takes affirmative action in support of this policy to employ and advance in employment individuals who are minorities, women, disabled, and veterans.
Click here for Metagenics’ California Data Privacy Act Disclosure #LI-POST**IND123
Salary Context
This $130K-$174K range is above the median for Data Engineer roles in our dataset (median: $140K across 33 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Metagenics, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 14% below the category median. Disclosed range: $130K to $174K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Metagenics AI Hiring
Metagenics has 1 open AI role right now. They're hiring across Data Engineer. Based in Jersey City, NJ, US. Compensation range: $174K - $174K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.