Azure Databricks Data Architect

Ascendum Solutions; Cincinnati (OH); On-site; USD 140,000 - 170,000; Full time

Job summary

Ascendum Solutions is seeking a Senior Data Architect to lead the design and optimization of Databricks Lakehouse solutions in cloud environments, preferably Azure. You will collaborate with clients and cross-functional teams to define scalable architectures and govern data strategies.

You will mentor engineers, drive workshops, and ensure delivery aligns with enterprise standards while promoting modular design and reusable components across platforms.

Qualifications

• 8+ years in data architecture or large-scale data integration.
• 3+ years delivering production-ready Databricks solutions.
• Strong Python, SQL, and data modeling skills.
• Experience with Azure Data Factory, Synapse, and DevOps tooling.

Responsibilities

• Architect scalable, high-performing data solutions to meet functional and non-functional requirements.
• Own architecture, design, and optimization of Databricks platforms (Azure preferred).
• Lead architecture workshops and sessions with clients to design scalable Databricks Lakehouse solutions.
• Define end-to-end data architectures with business and technical stakeholders; defend designs.
• Lead hands-on implementation of data pipelines and infrastructure using DevOps and IAC best practices.
• Mentor engineers and guide delivery teams on patterns, tools, and standards.
• Review architecture documents and ensure alignment with enterprise standards.

Skills

Databricks architecture

Python

SQL

Data modeling

Azure services

Terraform

ARM templates

CI/CD pipelines

Agile / Scrum

Communication

MLOps familiarity

Education

Bachelor's or Master's degree in CS/IT

Tools

Terraform

ARM templates

Job description

• Architect scalable, high-performing data solutions to meet both functional and non-functional requirements.
• Own the architecture, design, and optimization of Databricks platforms within cloud-based ecosystems (Azure preferred).
• Serve as the technical lead and trusted advisor for clients, facilitating architecture workshops, requirements discovery sessions, and whiteboard discussions to design scalable Databricks Lakehouse solutions that align with business objectives, data strategy, and modernization initiatives.
• Partner directly with business and technical stakeholders to define end-to-end data architectures, presenting and defending solution designs while providing hands-on guidance for Databricks, Spark, Delta Lake, cloud platforms (Azure/AWS/GCP), data governance, and CI/CD best practices throughout the project lifecycle.
• Lead hands-on implementation of data pipelines and infrastructure using best practices for DevOps and infrastructure-as-code.
• Partner with data engineering teams to ensure platform performance, reliability, and maintainability.
• Integrate Databricks solutions across systems, aligning with enterprise architecture standards and delivery best practices.
• Create and review architecture and solution design documents for large-scale data initiatives.
• Evangelize reuse and modular design through shared services and common data models.
• Enforce architectural standards and guide teams on patterns, tools, and delivery methods.
• Mentor and provide technical guidance to engineers during development and delivery.
• Contribute to risk management through proactive identification and mitigation of technical delivery risks.
• Operate at varying levels of abstraction - solutioning high-level architecture while diving deep when needed.

Required Qualification:

• Bachelor's or master's degree in computer science, Information Technology, or related field.
• 8+ years of experience in data architecture, cloud data warehousing, or large-scale data integration.
• 3+ years of hands-on experience delivering production-ready Databricks solutions.
• Strong proficiency in Python, SQL, data modeling, and Azure services (Data Factory, Event Hub, Synapse, DevOps).
• Experience implementing infrastructure using Terraform, ARM templates, or similar IAC tools.
• Comfort working in Agile/Scrum environments and supporting CI/CD pipelines.
• Excellent communication skills - you can explain complex ideas to tech teams and business stakeholders alike.
• Bonus: Familiarity with ML lifecycle concepts and MLOps tools, even if you're not a model builder yourself.