Enterprise Lead Data Engineer - Altorfer - CAT
Listed on 2026-09-07
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IT/Tech
Data Engineering, Data Warehousing
Position Summary
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.
Enterprise Data Platform Ownership- 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 Lake houses, Warehouses, Data Pipelines, Dataflows Gen2, and One Lake 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, Power Shell, and transformation logic as needed.
- Troubleshoot and resolve production data pipeline failures, mapping issues, performance issues, and integration errors.
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