Morton is seeking an experienced, hands-on Data Architect to join a growing enterprise IT team. This individual will be responsible for designing, implementing, and optimizing a modern Data Lakehouse environment supporting enterprise reporting, business intelligence, analytics, and AI initiatives.
The Data Architect will partner closely with the organization's Data Platform Lead, business stakeholders, and development teams to translate business requirements into scalable, secure, and maintainable data solutions.
This is a highly technical role requiring someone comfortable owning the architecture while remaining directly involved in data modeling, pipeline development, system integrations, and platform implementation.
Key Responsibilities
Design, implement, and maintain enterprise Data Lakehouse architecture using modern cloud technologies.
Develop scalable data models, structures, pipelines, and integration patterns supporting reporting, analytics, and AI initiatives.
Configure and maintain data integrations across enterprise applications, relational databases, APIs, and other data sources.
Collaborate with the Data Platform Lead to translate business and reporting requirements into technical solutions.
Develop and optimize ETL/ELT processes, data transformations, and data validation procedures.
Establish best practices for data quality, security, performance, governance, and platform reliability.
Partner with BI developers, software engineers, and AI teams to ensure enterprise data is accessible and structured appropriately.
Troubleshoot integration challenges, optimize platform performance, and document architectural designs and data flows.
Provide technical guidance throughout the implementation and ongoing evolution of the Data Lakehouse environment.
Required Qualifications
Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field, or equivalent experience.
5+ years of experience in data architecture, data engineering, data warehousing, or enterprise data integration.
Hands-on experience with Microsoft Azure and Databricks.
Demonstrated experience designing and implementing enterprise data architectures, including modern Data Lakehouse solutions.
Advanced SQL proficiency and strong understanding of relational and dimensional data modeling.
Experience designing and building ETL/ELT pipelines, data transformations, and integrations.
Experience integrating data from enterprise applications, APIs, databases, and multiple source systems.
Understanding of data governance, security, access controls, data lineage, and data quality practices.
Ability to translate business requirements into practical technical solutions and communicate effectively with stakeholders.
Preferred Qualifications
Experience with Microsoft Fabric, Azure Data Factory, and Azure Data Lake Storage.
Python, PySpark, or similar data engineering programming experience.
Experience with Power BI and semantic data models.
Familiarity with orchestration, monitoring, incremental loading, and pipeline optimization.
Experience with Git, Azure DevOps, and CI/CD practices.
Experience supporting AI, machine learning, or advanced analytics initiatives.
Background in professional services, legal technology, financial services, or other regulated environments.
Relevant Azure, Databricks, or data engineering certifications.
Ideal CandidateThe ideal candidate combines strong data architecture expertise with hands-on engineering capabilities. They have experience building and integrating modern cloud data platforms, can independently solve complex data challenges, and enjoy collaborating with technical and business teams to deliver enterprise-scale solutions.