Enterprise Lead Data Engineer

Altorfer CAT•Cedar Rapids, IA

1d•$100,000 - $170,000•Hybrid

About The Position

The Enterprise Lead Data Engineer is responsible for the design, development, governance, and ongoing operation of Altorfer’s enterprise data platform. This hands-on technical role serves as the owner of enterprise data acquisition, integration, transformation, and storage across Microsoft Fabric, Dataverse, Dynamics 365 Finance & Operations, Dynamics 365 Customer Engagement, Power Platform, and other business systems. This individual will lead the implementation and evolution of Altorfer’s Medallion Architecture strategy and ensure that enterprise data is available, accurate, governed, secure, and optimized for analytics, artificial intelligence, automation, Copilot, and reporting. The Enterprise Lead Data Engineer owns the foundational data layer of the organization. While partnering closely with reporting and analytics teams, this position is not primarily responsible for building dashboards or reports. Instead, the role ensures trusted, governed, and performant data is available for downstream business intelligence, AI, and operational workloads.

Requirements

Bachelor’s degree in Information Technology, Computer Science, Data Management, Engineering, or a related field, or equivalent work experience.
Five or more years of experience in data engineering, data architecture, enterprise data platforms, or related technical roles.
Hands-on experience designing, building, and supporting ETL/ELT pipelines and enterprise data integrations.
Strong understanding of data modeling, data warehousing, data governance, and data quality management.
Demonstrated ability to work with technical teams, business stakeholders, vendors, and project teams across the organization.
Ability to manage priorities, troubleshoot complex issues, document solutions, and support business-critical systems.
Microsoft Data Platform: Microsoft Fabric, OneLake, Data Factory, Dataflows Gen2, Lakehouse, Warehouse, Dataverse, Power Platform, Azure Data Services
Data Engineering: ETL/ELT design, Medallion Architecture, data modeling, data warehousing, data governance, data quality, metadata, lineage, master data concepts
Development: SQL, Python, PowerShell, Spark, REST APIs, JSON, data transformation development, scripting, automation
Enterprise Applications: Dynamics 365 Finance & Operations, Dynamics 365 Customer Engagement, ERP data integration, CRM data integration, operational system data integration

Nice To Haves

Experience implementing Microsoft Fabric in an enterprise environment.
Experience with Dynamics 365 Finance & Operations and Dynamics 365 Customer Engagement data integration.
Experience designing and supporting Dataverse for enterprise applications.
Microsoft Fabric, Azure Data Engineer, Power Platform, or related Microsoft certifications.
Experience supporting AI, Copilot, machine learning, or advanced analytics data initiatives.
Experience leading enterprise data modernization or cloud-first transformation initiatives.

Responsibilities

Own the enterprise data platform built on Microsoft Fabric and related Microsoft data services.
Develop and maintain enterprise data architecture standards, patterns, and operating procedures.
Define and implement data engineering best practices for ingestion, transformation, storage, governance, and security.
Ensure enterprise data is reliable, scalable, secure, well governed, and optimized for downstream consumption.
Serve as the technical lead for data platform design decisions and enterprise data modernization initiatives.
Design, build, and maintain Microsoft Fabric Lakehouses, Warehouses, Data Pipelines, Dataflows Gen2, and OneLake structures.
Implement and manage Medallion Architecture, including Bronze, Silver, and Gold data layers.
Develop repeatable ingestion, transformation, and orchestration frameworks for enterprise data domains.
Optimize data storage, compute, performance, reliability, and cost efficiency across Fabric workloads.
Establish standards for data domains, naming conventions, data lineage, metadata, and reusable data products.
Design, develop, test, deploy, monitor, and support ETL and ELT pipelines.
Build integrations between enterprise business applications and Microsoft Fabric.
Develop ingestion frameworks using APIs, connectors, files, database extracts, and other integration patterns.
Write and maintain SQL, Python, Spark, PowerShell, and transformation logic as needed.
Troubleshoot and resolve production data pipeline failures, mapping issues, performance issues, and integration errors.
Automate manual data acquisition, cleansing, validation, and transformation processes wherever possible.
Lead data integration efforts related to Dynamics 365 Finance & Operations.
Lead data integration efforts related to Dynamics 365 Customer Engagement.
Design and maintain Dataverse ingestion, synchronization, transformation, and governance processes.
Develop data models supporting ERP, CRM, Power Platform, automation, and operational workloads.
Ensure business-critical Dynamics 365 and Dataverse data is available, trusted, and usable across the enterprise data platform.
Collaborate with Power Platform developers to ensure data consistency, scalability, security, and platform standards.
Establish and enforce enterprise data quality standards, validation rules, and remediation processes.
Implement data lineage, cataloging, metadata management, and documentation practices.
Develop monitoring and alerting for data quality, pipeline health, platform performance, and operational availability.
Partner with business teams to define data ownership, stewardship, retention, and lifecycle practices.
Support regulatory, compliance, privacy, and security requirements for enterprise data assets.
Work with cybersecurity and infrastructure teams to ensure proper access controls, identity governance, and secure data handling.
Build data pipelines and integration processes directly within Microsoft Fabric and related services.
Create and maintain transformation logic, data models, SQL queries, stored procedures, notebooks, scripts, and automation.
Configure and support Fabric workloads, Dataverse integrations, connectors, APIs, and data movement processes.
Troubleshoot production issues, failed refreshes, integration errors, mapping problems, and data quality exceptions.
Continuously improve performance, reliability, automation, documentation, and platform standards.