BI Data Engineer

Position: Data Engineer Location: Houston, TX 77002 (Hybrid, 3 4 days on-site) Duration: 6 Months

Position Summary:
The Data Engineer will support the Retail Business Intelligence team by designing and optimizing data architecture, models, and reporting solutions. This role involves hands-on data engineering, developing scalable data pipelines, and delivering business insights through visualization and analytics. The ideal candidate is highly analytical, experienced with cloud platforms, and proactive in improving data quality and operational processes.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines and data marts.

• Gather, clean, and integrate large data sets to meet evolving business needs.

• Implement automation and process improvements to optimize data delivery and infrastructure.

• Develop, maintain, and document data models, reports, and dashboards using tools such as Tableau or Power BI.

• Collaborate with business and analytics teams to gather reporting requirements and ensure data reliability.

• Apply data governance standards and best practices across data management processes.

• Perform ad hoc analysis and support marketing and business performance reporting.

• Provide updates on project progress and deliverables.

Required Qualifications:

• Bachelor s or Master s degree in Computer Science, Information Systems, or a related field.

• 4 years of experience in data engineering, business intelligence, or analytics.

• Strong programming skills in Python, R, or SAS.

• 3 years of SQL development and data modeling experience.

• Proficiency in AWS, Databricks, and cloud-based data ecosystems.

• Hands-on experience with data visualization tools such as Tableau or Power BI.

• Familiarity with relational and non-relational databases.

• Proven ability to work with large, complex datasets and derive actionable insights.

Preferred Qualifications:

• Experience in the gas or power sector, particularly the retail electricity market.

• Skills in root cause analysis and large-scale data manipulation.

• Strong organizational abilities and comfort working with cross-functional teams.

• Demonstrated adaptability to new technologies and ambiguous problem spaces.

Work Conditions:

• Hybrid schedule: 3 4 days in-office per week.

• Occasional overtime based on project demands.