Databricks Architect

Job Title: Enterprise Data Architect (Contract to Hire)

Location: 100% Remote

Duration: 5-Month Contract to Hire (Conversion to Sr. Director level)

Position Type: Full-Time Contract-to-Hire

Role Overview

We are seeking a visionary Enterprise Data Architect to play a critical role in shaping our organization's data future. In this high-impact, highly visible role operating at the intersection of strategy and execution, you will define and drive the enterprise data architecture strategy, establish a scalable data architecture framework, and lead the design and stand-up of a modern data platform in close partnership with IT.

Key Responsibilities:

Enterprise Data Architecture Strategy

• Define and own the enterprise data architecture framework, including data domains, data flows, integration patterns, and governance guardrails.

• Establish architectural standards, principles, and best practices that guide how data is modeled, stored, accessed, and governed across the enterprise.

• Serve as the architectural authority on data-related decisions across business systems and platforms.

Data Platform Build-Out

• Lead the architecture and design of our cloud-based data platform on Azure and Databricks, ensuring it is scalable, secure, and fit for enterprise-grade analytics and AI/ML workloads.

• Partner closely with IT to translate architectural blueprints into implemented and scaled production-ready platform capabilities.

• Define integration architectures connecting core systems including SAP S/4HANA, SAP Business Data Cloud (BDC), Salesforce CRM, Siemens TeamCenter X, and Informatica.

Cross-Functional Collaboration

• Engage with business stakeholders, data product owners, and technology teams to understand requirements and ensure architecture decisions align with business priorities.

• Work in partnership with the VP of Data and CDO to align data platform capabilities with the broader enterprise data strategy.

• Provide architectural guidance to data engineering, analytics, AI, and governance teams.

• Lead Proof of Concepts (POC), Proof of Values (POV), and Pilots to evaluate technology fit, cost, viability, and adoption.

Governance & Data Quality

• Design and embed data governance, metadata management, and data quality frameworks into the architecture using Informatica and aligned tooling.

• Define master data architecture patterns across key domains (Customer, Product, Supplier, etc.).

Qualifications

Required Experience:

• 10+ years of experience in data architecture, data engineering, or a related discipline.

• Proven experience designing and implementing enterprise data platforms on Azure (Data Lake, Synapse, Data Factory, etc.) and Databricks.

• Strong experience integrating complex ERP and CRM systems, particularly SAP S/4HANA and Salesforce.

• Hands-on experience with Informatica for data integration, data quality, and/or master data management (MDM).

• Demonstrated ability to build enterprise data and AI architecture frameworks from the ground up not just inherit and maintain existing ones.

• Strong communication skills with the ability to translate complex architectural concepts for both technical and executive audiences.

• Demonstrated team leadership and the ability to guide junior data engineers, analysts, and architects.

Preferred Experience:

• Experience with SAP Business Data Cloud (BDC) and/or Siemens TeamCenter X, STIBO MDM.

• Background in manufacturing, industrial automation, or B2B technology environments.

• Experience working in or alongside a formal data governance program.

• Familiarity with data mesh, data product, or domain-oriented architecture patterns.

• Familiarity with Digital Thread and Digital Twin concepts and architecture patterns.

• Cloud or Architectural certifications (e.g., Azure Solutions Architect, TOGAF).

What Success Looks Like in Year One:

• An enterprise data architecture framework is defined, documented, and socialized across the organization.

• The cloud data platform (Azure/Databricks) is architected and in active build in partnership with IT.

• Integration architecture is defined for all major source systems.

• Architectural standards are in place and being actively adopted by data engineering and product teams.