Data Architect

This advisor-level role is responsible for advancing the architecture, scalability, and maturity of the enterprise data platform within the Enterprise Data and Analytics team.

The Data Architect defines and evolves platform capabilities that support analytics, data engineering, machine learning operations (MLOps), and emerging AI-driven use cases, ensuring solutions are scalable, reliable, and aligned with business needs.

This role partners with cross-functional teams to establish standards, patterns, and platform capabilities that enable efficient delivery of data products, machine learning models, and advanced analytics solutions across the organization.

Job Duties & Responsibilities

Data Platform Architecture

• Define and evolve data platform architecture, ensuring scalability, reliability, and long-term sustainability
• Establish standards and best practices for data modeling, data pipelines, and platform usage
• Drive maturity across data platform capabilities, including ingestion, transformation, serving, and governance

MLOps Enablement

• Lead design and implementation of MLOps capabilities, including model deployment, monitoring, and lifecycle management
• Establish reusable patterns and frameworks supporting machine learning and data product delivery
• Support teams in operationalizing machine learning solutions at scale

Emerging Capabilities & Platform Evolution

• Evaluate and enable platform capabilities to support evolving AI and advanced analytics use cases
• Ensure the platform can support new workloads and architectural patterns while maintaining stability and performance
• Recommend tools, technologies, and process improvements to enhance delivery and platform effectiveness

Collaboration & Technical Leadership

• Partner with business stakeholders and technical teams to define and deliver scalable solutions
• Provide architectural guidance across multiple teams to ensure consistency and alignment
• Promote best practices in software engineering, DevOps, and SDLC processes

• Strong proficiency in Python with experience building and operating production data or ML systems
• Strong proficiency in SQL and experience with distributed data processing frameworks
• Expertise in data architecture, data modeling, and data platform design
• Experience designing and supporting MLOps environments, including pipeline automation, deployment, and monitoring
• Strong software engineering fundamentals, including object-oriented design, unit testing, exception handling, and use of common design patterns.
• Expertise in data modeling, data warehousing, and ETL/ELT processes supporting analytics and machine learning cases.
• Hands-on experience with cloud-based data platforms and architectures, including Snowflake and Databricks.
• Strong knowledge of CI/CD, DevOps, and release management practices used to deploy and operate production data and machine learning solutions.
• Strong knowledge of SDLC processes, including Agile methodologies.
• Ability to define and implement standards, patterns, and reusable frameworks
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

Education

Minimum: High school diploma or GED

Preferred: Bachelor's degree in Information Systems, Computer Science, or related field

Experience

Minimum: 8 years related work experience

Preferred: Related work experience in data engineering, software engineering, or data architecture

Our core values - Stewardship, Character, Collaborate, Learn, Disrupt - are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expand's performance among our E&P competitors.