Senior Data Engineer

Ezee Fiber is seeking a Senior Data Engineer to help design, build, and optimize scalable data infrastructure and business logic that powers analytics, reporting, and network and customer insights. In this Houston, TX (onsite) role, you will ensure data solutions are accessible, secure, accurate, governed, and actionable, while providing technical leadership across data initiatives.

Responsibilities

• Design, develop, and optimize scalable ETL/ELT pipelines that integrate data from CRM, OSS/BSS, GIS, network monitoring tools, REST APIs, and other enterprise platforms.
• Write efficient, reusable, production-quality Python and SQL to process, transform, validate, and serve large and complex datasets.
• Lead automated data-quality controls, anomaly detection, monitoring, alerting, and remediation processes.
• Design and optimize data models, data lakes, data warehouses, and related architecture for business intelligence, analytics, and operational use cases.
• Own and enhance data infrastructure across cloud and on-premise environments, including AWS, Azure, Google Cloud, MS SQL, and PostgreSQL.
• Evaluate pipeline and platform performance, identify scalability or reliability risks, and implement improvements to increase resiliency and efficiency.
• Establish and promote engineering standards across data pipelines, schemas, documentation, testing, deployment, observability, and version control.
• Partner with Engineering, Field Operations, Customer Experience, Finance, Marketing, and other stakeholders to translate complex requirements into scalable data solutions.
• Provide technical leadership on data initiatives, including solution design, architecture decisions, code reviews, troubleshooting, and guidance for less-experienced engineers or technical partners.
• Define and maintain trusted datasets and data products used for dashboards, performance metrics, forecasting, and executive decision-making.
• Ensure data solutions comply with Ezee Fiber standards for security, privacy, compliance, access controls, and data governance.
• Support data lineage, metadata, documentation, and data-integrity practices across systems and applications.
• Lead or support integration of real-time or near-real-time data using Kafka, Spark, APIs, or comparable tools.
• Identify opportunities to automate manual processes, reduce technical debt, and improve the quality, speed, and reliability of data delivery.
• Independently diagnose and resolve complex production data issues, communicating risks, impacts, and recommended solutions to technical and business stakeholders.

Technologies

• Python, SQL, AWS, Azure, Google Cloud, MS SQL, PostgreSQL
• Kafka, Spark, REST APIs, APIs