Data Architect

Niron Magnetics, Inc.•Minneapolis, MN

22h•$165,000 - $190,000

About The Position

Niron Magnetics is seeking a Sr. Enterprise Data Architect to define and evolve the data architecture that underpins manufacturing, R&D, and business operations as we scale toward high-volume production. This role owns how the estate matures from the current MS fabric foundation, scaling and expanding the platform, and evaluating and deploying additional environment in the data estate as well as complementary data tools as the business requires. This is a senior, hands-on technical leadership role for someone excited to scale and mature a foundational data platform in a high-growth, deep-tech manufacturing environment operating under strict compliance requirements.

Requirements

10+ years of experience in data engineering, data platforms, or enterprise/data architecture, with demonstrated architectural ownership of production systems.
Deep expertise designing modern data estate architecture, dimensional and canonical data modeling, and semantic layers.
Deep hands-on experience with a modern cloud data platform, specifically Microsoft Fabric and the Azure data stack experience.
Experience with Databricks, specifically in a controlled-data environment, is strongly desired.
Demonstrated experience integrating enterprise systems and building data pipelines via REST, GraphQL, message queues, webhooks, and ETL/ELT tooling.
Strong grounding in cloud architecture (Azure preferred), and in data governance disciplines—quality, lineage, cataloging, MDM, and access control.
Proficiency in SQL and Python; familiarity with R and/or Spark a plus.
Experience integrating with or sourcing data from ERP (D365 F&O or comparable) and operational/industrial systems preferred.
Ability to balance near-term delivery with long-term architectural vision.
Excellent communication skills and comfort operating in ambiguous, early-stage environments.

Nice To Haves

Experience in manufacturing, industrial, or deep-tech environments.
Experience working within a regulated or a compliance-driven environment.
Advanced degree in Computer Science, Engineering, Data Science, or a related field.
Experience with time-series, OT/sensor, or manufacturing process data.
Familiarity with enabling AI/ML and RAG workloads on top of a governed data estate.
Passion for clean tech, sustainable materials, or climate-positive innovation.

Responsibilities

Design and own the end-to-end enterprise data architecture spanning ingestion, storage, modeling, transformation, governance, and consumption.
Build on the existing Microsoft Fabric platform and initial data lakehouse, maturing the architectural patterns, standards, and reference designs that make the estate scalable, secure, and maintainable.
Assess where the current lakehouse foundation should expand, and evaluate, select, and deploy additional data estate stores, and complementary data tooling to meet analytics, reporting, and AI needs across the business.
Define canonical data models, master data management, and semantic layers that create a single, trusted source of truth across the business.
Set the roadmap for evolving the data estate as Niron scales, sequencing work to deliver near-term value while protecting long-term architectural integrity.
Partner with Manufacturing, R&D, Quality, Finance, and Operations to identify high-value data and analytics use cases (e.g., production reporting, yield and quality analysis, cost and supply insights).
Build the data foundations that power business intelligence, self-service analytics, and downstream AI/ML systems.
Design robust integration between core systems—ERP (D365 Finance & Operations), MES/manufacturing systems, laboratory tools, and enterprise SaaS—via APIs, message queues, and ETL/ELT pipelines.
Translate business and technical requirements into resilient, well-documented data designs.
Own data governance: data quality, lineage, cataloging, classification, retention, and access control.
Partner with IT and Security to ensure the data estate meets reliability, security, and regulatory compliance obligations, including handling of controlled and export-regulated data.
Design data segregation and controls appropriate to a multi-tenant, compliance-sensitive environment.
Act as the technical authority and advisor on data architecture, best practices, and trade-offs.
Review designs, mentor data and analytics engineers, and raise the technical bar across teams.
Support build-vs-buy decisions and evaluate vendors, tools, and platforms.
Communicate complex technical concepts clearly to non-technical stakeholders and influence without authority.