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# Data Engineer (AI Engineering)

- **Company:** Metagenics, Inc.
- **Location:** Jersey City, NJ, United States
- **Experience:** Experienced
- **Salary:** $130,000.0 - $174,870.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Big Data, Computer Programming, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Python (Programming Language), Machine Learning, Automation of Marketing, Operational Databases, SQL Databases, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Generative AI, Microsoft Fabric, Microsoft Copilot Studio, Pyspark, Microsoft Dynamics 365 Finance & Operations, Machine Learning Operations, Azure Synapse Analytics, Data Pipelines
- **Published:** October 10, 2026
- **Apply:** https://www.careerjet.com/job/use799966419d4a4dd57d43aa802cbb6d2/eaa

## About the Role

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.

## Description

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., * 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.