Data Engineering Lead

Expand Energy•Oklahoma City, TX

About The Position

The Lead Data Engineer is a senior technical role responsible for the design, development, operation, and continuous improvement of Expand Energy’s enterprise data platform and data engineering ecosystem. This position owns critical data pipelines, integration frameworks, and enterprise data models that power analytics, reporting, operational processes, data science, and emerging AI capabilities across the organization. The Lead Data Engineer combines deep expertise in data engineering, data modeling, and data warehousing with strong operational ownership of a modern cloud data platform. This role is responsible for ensuring enterprise data assets remain trusted, scalable, secure, observable, and highly available. In addition to operating and enhancing existing capabilities, this role helps evaluate and adopt emerging platform features that expand the value of enterprise data assets and support the future direction of the Data & Analytics organization.

Requirements

Advanced SQL development, query optimization, and performance tuning experience.
Deep understanding of data modeling concepts including dimensional, canonical, normalized, and denormalized data models.
Strong knowledge of data warehousing principles, cloud data platforms, and modern data architecture patterns.
Experience designing and supporting enterprise data pipelines, ETL/ELT processes, and data integration solutions.
Experience developing trusted, reusable data assets that support analytics, reporting, machine learning, and AI use cases.
Strong understanding of data quality, governance, lineage, and metadata management concepts.
Strong hands-on experience with Snowflake or a comparable enterprise cloud data platform.
Experience with modern data integration and transformation technologies such as dbt, Fivetran, Informatica, HVR, Azure Data Factory, or equivalent platforms.
Experience supporting structured, semi-structured, and unstructured data formats including JSON, XML, Parquet, Avro, and CSV.
Working knowledge of Snowflake administration, resource governance, role-based access control (RBAC), query optimization, and secure data sharing.
Familiarity with semantic models, data products, AI-enabled analytics, and emerging cloud data platform capabilities.
Strong experience utilizing Azure DevOps for source control, backlog management, release management, and deployment automation.
Experience with Git, pull requests, code reviews, and software development lifecycle best practices.
Ability to establish engineering standards, reusable frameworks, and development patterns that improve solution quality and delivery consistency.
Minimum: High school diploma or GED
Minimum: 8 years related work experience

Nice To Haves

Bachelor’s degree in Information Systems, Computer Science, or related field
Snowflake certification (SnowPro Core, SnowPro Advanced, or equivalent).

Responsibilities

Design, develop, and maintain enterprise data pipelines, integration frameworks, dimensional and canonical data models, and cloud data warehouse solutions that serve as the foundation for enterprise analytics, reporting, machine learning, and AI capabilities.
Build and operate scalable data pipelines utilizing batch, CDC, streaming, API-based, event-driven, and log-based data movement patterns while ensuring reliability, performance, and data quality.
Partner with business and technology teams to onboard new data sources, deliver trusted data assets, and establish scalable data engineering solutions aligned with enterprise architecture standards.
Serve as a lead technical contributor on strategic initiatives that rely on enterprise data assets and integration capabilities.
Advise project teams on data architecture, integration patterns, platform capabilities, and engineering best practices.
Mentor data engineers and promote operational excellence across the Data & Analytics organization.
Collaborate closely with architects, analytics teams, application teams, cybersecurity, infrastructure, and business stakeholders.
Identify opportunities to leverage emerging data platform capabilities to improve business outcomes and increase organizational agility.
Balance immediate delivery needs with long-term platform sustainability, scalability, and maintainability.
Support and administer core Snowflake platform capabilities including compute management, resource governance, workload optimization, and security controls.
Design and maintain role-based access control (RBAC) frameworks and data access patterns supporting enterprise governance requirements.
Evaluate new Snowflake features and capabilities and recommend adoption strategies where appropriate.
Help evolve platform capabilities supporting analytics, AI, agentic workflows, semantic models, data products, and future business use cases.
Establish and promote engineering standards for source control, testing, deployment, documentation, and support.
Leverage Azure DevOps for backlog management, source control, release management, and deployment automation.
Perform code reviews and provide technical guidance to other team members.
Contribute reusable frameworks, templates, utilities, and engineering patterns that improve delivery consistency and speed.