Data Engineer

CB&I•The Woodlands, TX

About The Position

The Data Engineer designs, develops, integrates, and optimizes enterprise data platforms, pipelines, and data models that support reporting, analytics, automation, and AI initiatives. The role reports directly to the Director, Applications and Data Management and routinely interacts with Global IT, Global Business Leads, Global Functional Leads, and Project Management. The position establishes data management requirements and selects appropriate platform technologies; leads the design of scalable, efficient data pipelines and engineering standards; and develops practices for data quality, security, integration, governance, and compliance. It partners with infrastructure, cybersecurity, analysts, and business stakeholders to deliver reliable datasets and support analytics and machine learning. The role also optimizes data systems, automates processes, documents architectures and pipelines, and provides technical leadership across initiatives.

Requirements

Bachelor’s degree in computer science, data engineering, information systems, or equivalent work experience.
15 years of general data and application experience, including 10 or more years specifically in data engineering.
Hands-on experience with ETL/ELT processes, data modeling, and cloud platforms.
Strong expertise in programming languages such as Python, Java, or C#.
Strong expertise querying databases with SQL.
Extensive experience with data pipeline tools such as Informatica, Azure Data Factory, or Apache Kafka.
Experience with database management systems such as SQL Server, Oracle, or MongoDB.
Expertise architecting cloud data platforms such as Snowflake, Microsoft Synapse, or Databricks.
Familiarity with reporting and visualization tools such as Power BI, Tableau, or Business Objects.
Demonstrated experience delivering data engineering projects in a team setting.
Ability to communicate clearly and concisely, orally and in writing, and lead presentations, training courses, and effective meetings.
Experience managing service-provider relationships, such as data engineering consultants and cloud data platform vendors.
Expertise in data engineering, integration, and data architecture best practices.
Knowledge of data modeling, data warehousing, and master data management principles.
Ability to design scalable, secure, and reliable data solutions.
Proficiency with cloud data platforms, databases, APIs, and ETL/ELT technologies.
Strong understanding of data governance, security, privacy, and compliance requirements.
Ability to analyze complex technical challenges and implement effective solutions.
Experience leading cross-functional initiatives and collaborating with business and technology stakeholders.
Commitment to continuous improvement, automation, and technical excellence.
Proactively embrace and promote company values and culture, including a healthy and safe work environment.

Nice To Haves

Master’s degree in computer science, data engineering, or information systems.
Experience in the Architecture, Engineering and Construction industry.
Relevant data engineering certifications.

Responsibilities

Establish and assess organizational data management requirements and select and implement suitable data platform technologies.
Lead the design of robust, scalable, and efficient data pipelines and set data architecture and engineering standards across data sources.
Establish best practices for data quality and security to support enterprise data integration strategies.
Collaborate with infrastructure and cybersecurity teams to architect and secure data infrastructure.
Work with data analysts and business stakeholders to understand data needs, provide clean and reliable datasets, and support machine learning and analytics initiatives.
Establish and optimize data systems for performance, scalability, and cost-efficiency; troubleshoot and resolve pipeline issues.
Design and implement data models, including star schema and dimensional models, for reporting and analytics.
Automate repetitive data processes to improve efficiency and reduce manual intervention.
Create and maintain documentation for data pipelines, processes, and system architectures.
Advise on data governance, regulatory compliance, data stewardship, and risk management practices.
Provide technical leadership across multiple initiatives and influence larger, long-term projects.