Lead Data Engineering Architect

Overview
Summary

The Lead Data Engineering Architect drives the design, build, and management of the enterprise data warehousing infrastructure, API integration layers, and foundational pipelines. This role is strictly dedicated to core data engineering and backend enablement, operating completely distinct from front-end BI or Data Analytics reporting architecture. The architect defines how data is extracted from critical operational systems, transformed into optimized structures, and safely staged to supply downstream analysis layers. This role also serves as the central coordination anchor for critical data enablement decisions spanning Integration APIs, warehousing infrastructure, and analytical ecosystems.

Responsibilities
Essential Functions

Designs and builds robust enterprise data models (Lakehouse and Data Warehouse frameworks) utilizing Microsoft Fabric and SQL Server ecosystems.
Develops ingestion and consumption pipeline architectures to harvest data efficiently from foundational source systems (including CRM, ERP, managed services, professional services, and HR platforms).
Architects and delivers robust API integration layers to facilitate real-time and batch-driven data exchanges across internal and external software systems.
Coordinates critical data enablement decisions across integration APIs, warehousing structures, and downstream analytics platforms to ensure absolute systemic alignment.
Serves as the primary structural architect for the Microsoft Fabric platform, deploying Microsoft foundational tools to enforce consistent organizational standards.
Establishes backend master data schemas, enterprise data integration rules, pipeline automation frameworks, and technical governance guidelines.
Determines pipeline and storage structural requirements by evaluating cross-departmental operations, SaaS APIs, and underlying source database systems.
Calculates optimum data processing parameters and implements incremental loading routines, schema-drift protection controls, and performance optimization mechanics.
Participates in database code walk-throughs, query tuning sessions, and data orchestration workflow evaluations to ensure highly scalable, low-latency execution.
Collaborates closely with the Data Engineering Team and Data Engineering Manager to guide development sprints and convert structural system blueprints into actionable sprint tasks.
Partners with the BI Architect and Data Analytics Team to seamlessly support delivery and structural optimization needs feeding into the downstream BI layer.
Accomplishes information systems and organization mission by completing related architectural and enablement goals as needed.

Additional Specific Duties and Responsibilities

Facilitates technical collaboration across development teams, business applications specialists, and backend operational units to streamline data accessibility.
Identifies, monitors, and communicates technical risk factors, integration bottlenecks, pipeline vulnerabilities, and architecture deployment status to IT management.
Reports operational achievements and systemic roadmaps directly up to the Director of Data Systems and Enablement.

Qualifications
Required Qualifications

Bachelor's in computer science, Information Systems, or equivalent direct practical work experience.
6+ years of dedicated experience working in data engineering environments, with a minimum of 2+ years leading enterprise-scale pipeline and API architecture.
Proven technical experience designing, building, and implementing enterprise data solutions natively within Microsoft Fabric and the broader Microsoft data stack.
Strong understanding of API-driven data extraction techniques, web service protocols (REST, SOAP), modern data orchestration tools, data warehouse modeling theories, and cloud infrastructure patterns.
Working technical experience integrating enterprise software platforms, specifically NetSuite, Microsoft CRM, ConnectWise, Ivanti, ADP, Keka, and SharePoint integration lines.
Good written and oral communication skills with a proven capability to articulate abstract data structures and API behaviors in clear, user-friendly language.
Strong technical documentation skills applied to tracking data lineage, API contracts, dictionary compliance, and structural layouts.
Ability to effectively prioritize and coordinate tasks in a fluid, flexible hybrid/remote environment.