Master Data Engineer

Marco Technologies•REMOTE, OH

2d•$80,796 - $125,234•Remote

About The Position

The Master Data Engineer owns the accuracy, consistency, and governance of Marco’s core master data by building and operating pipelines across ERP, CRM, Microsoft Fabric data warehouse, and other system repositories. The role establishes and enforces Marco’s formal data ontology in partnership with the data architecture team, and transforms raw operational data into governed, machine-readable master data through engineered pipelines. It works directly with Finance, Sales, Procurement, Internal Systems, and Business Unit leadership to maintain clean, de-duplicated, thoroughly documented master data and aligned definitions across the organization. The position serves as the primary engineer and steward for customer, item/equipment, vendor, and location data. It develops automated validation, reconciliation, match/merge, and survivorship logic; maintains governed and auditable record-request workflows; documents standards, lineage, and stewardship procedures; supports cross-functional teams; and tracks data quality and pipeline performance KPIs. This is a remote-eligible position, but employees must reside in one of the specified states: DE, FL, IA, IL, IN, KY, MD, MI, MN, MO, ME, NE, ND, NJ, PA, RI, SD, TX, or WI.

Requirements

Bachelor’s degree in computer science, information systems, or a related field, or equivalent practical experience.
At least 7 years of experience in data engineering, master data management, or a related data-operations role, including setting standards or leading initiatives with minimal supervision.
Strong proficiency in Python, including PySpark, and SQL for pipeline development, data profiling, validation, and reconciliation.
Hands-on experience building and maintaining pipelines in Microsoft Fabric (Dataflows Gen2, notebooks, Lakehouse) or a comparable modern data platform such as Databricks or Snowflake.
Hands-on experience integrating MDM processes with ERP and/or CRM platforms such as e-Automate, Salesforce, or comparable systems.
Experience designing match/merge/survivorship logic and automated data quality rules.
Working familiarity with AI-assisted engineering tools such as Claude or Copilot for pipeline development and documentation.
Excellent attention to detail, documentation habits, and written and verbal communication skills with technical and non-technical stakeholders.
Demonstrated ability to lead cross-functional data governance and engineering initiatives and influence stakeholders without formal authority.

Responsibilities

Design, build, and maintain automated pipelines using Microsoft Fabric Dataflows Gen2, notebooks/PySpark, and SQL to consolidate and reconcile master data across e-Automate, Salesforce, and Marco’s Fabric data warehouse.
Serve as primary engineer and steward for customer, item/equipment, vendor, and location data, ensuring accuracy, completeness, and de-duplication at the source.
Define and enforce the formal data ontology, MDM standards, naming conventions, and business rules through automated validation and match/merge/survivorship logic.
Partner with the data architecture team to model business concepts and real-world entity relationships and turn raw data into machine-readable, queryable knowledge.
Lead recurring data quality audits and build automated profiling and reconciliation jobs across ERP, CRM, and Fabric repositories.
Engineer remediation workflows for ancillary data outside core systems.
Build and maintain an auditable intake and approval workflow for master-record creation, changes, merges, and deactivation, validating requests with the relevant departments before loading.
Document governance standards, field definitions, lineage, and stewardship procedures in a living data dictionary, and publish self-service documentation for business users.
Partner with the BI/reporting function to detect master data quality issues through automated checks before they affect downstream dashboards and reports.
Train and support Sales, Service, Procurement, and Finance teams on master data intake standards and the technical and business impacts of poor data quality.
Track and report data quality and pipeline KPIs, including duplicate rate, completeness, accuracy, cycle time, and pipeline SLA, to the Director of Enterprise Data and Analytics.