Data Engineer

Intuitive Machines LLC•Houston, TX

12d•Hybrid

About The Position

Intuitive Machines is seeking an experienced Data Engineer to lead the aggregation, integration, and administration of data across our enterprise manufacturing technology stack. This role can be based at our Bay Area facility or remotely in the US. This position will play a pivotal role in unifying data from our critical manufacturing systems—including CREO, TeamCenter, Solumina, SAP, and IFS—to enable analytics, reporting, and operational insights that drive spacecraft manufacturing excellence. We're looking for a hands-on expert who can design and implement data pipelines, establish database operational standards, optimize enterprise system performance, and ensure data integrity across our manufacturing ecosystem. If you have deep expertise in PLM, MES, and ERP systems, with a strong background in database administration and data engineering, we want to hear from you.

Requirements

Must be a US Citizen or US Permanent Resident (ITAR requirement)
Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or related field. Four additional years of experience may be substituted for a degree.
Minimum of 5 years of hands-on experience in data engineering or database administration roles.
Experience with PLM systems (TeamCenter, Windchill, or similar) and understanding of CAD data structures.
Experience with MES platforms (Solumina,Camstar, or similar) and manufacturing execution workflows.
Experience with ERP systems (SAP, IFS, Oracle EBS, or similar) and enterprise data models.
Proficiency in SQL and database technologies (Oracle, SQL Server, PostgreSQL).
Experience with ETL tools (Informatica, Talend, SSIS, or similar) or custom data pipeline development.
Experience in scripting languages (Python, PowerShell, or similar) for data automation.

Nice To Haves

Experience in aerospace, defense, or highly regulated manufacturing environments.
Knowledge of CREO Parametric and its data management integration withTeamCenter.
Experience with cloud data platforms (AWS Redshift/Glue, Azure Synapse, Snowflake, or Databricks).
Familiarity with data visualization tools (Tableau, Power BI, or similar).
Understanding of manufacturing data standards (MTConnect, ISA-95, or similar).
Experience with Agile development methodologies and DevOps practices for data infrastructure.
Professional certifications such as Oracle DBA, Microsoft Certified: Azure Data Engineer Associate, AWS Certified Database – Specialty, or similar.

Responsibilities

Design and implement data aggregation pipelines from CREO,TeamCenter,Solumina, SAP, and IFS into centralized data repositories.
Establishdata integration standards, processes, and documentation for enterprise manufacturing systems.
Develop ETL/ELT workflows to enable real-time and batch data synchronization across platforms.
Administer and optimized databases supporting TeamCenter,Solumina, SAP, and IFS environments.
Manage database performance tuning, backup and recovery procedures, and disaster recovery planning.
Monitor database health, implement security controls, and ensure high availability of manufacturing systems.
Serve as technical DBA for PLM (TeamCenter), MES (Solumina), and ERP (SAP, IFS) platforms.
Configure and maintaindatabase connections, user access controls, and system integrations.
Collaborate with application administrators to support system upgrades, patches, and migrations.
Design data models and schemas that support manufacturing analytics and business intelligence initiatives.
Establish data quality standards, validation rules, and master data management practices.
Implement data governance aligned with aerospace manufacturing compliance requirements (AS9100, ITAR, etc.).
Build data warehouses or data lakes to enable cross-system reporting and analytics.
Create data marts optimized for manufacturing KPIs, production metrics, and supply chain visibility.
Partner with business analysts and engineering teams to define reporting requirements and deliver actionable insights.
Implement monitoring and alerting for data pipeline health and database performance.
Continually evaluate data infrastructure for modernization, automation, and scalability improvements.
Document technical procedures, runbooks, and system architecture diagrams.