Senior Data Engineer

Design, build, and operate scalable data infrastructure and data products that power analytics, reporting, and network or customer insights.

Responsibilities

• Design, develop, and optimize scalable ETL/ELT pipelines integrating data from CRM, OSS/BSS, GIS, network monitoring tools, REST APIs, and other enterprise platforms.
• Write production-quality, reusable Python and SQL for processing, transforming, validating, and serving large, complex datasets.
• Lead automated data-quality controls, including anomaly detection, monitoring, alerting, and remediation workflows.
• Design and optimize data models, data lakes, data warehouses, and supporting 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 and reliability risks, and implement improvements to increase resiliency and efficiency.
• Establish and promote engineering standards for data pipelines, schemas, documentation, testing, deployment, observability, and version control.
• Partner with Engineering, Field Operations, Customer Experience, Finance, Marketing, and other stakeholders to translate business requirements into scalable data solutions.
• Provide technical leadership across data initiatives: solution design, architecture decisions, code reviews, troubleshooting, and guidance to less-experienced engineers or technical partners.
• Define and maintain trusted datasets and data products supporting 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 data work, reduce technical debt, and improve quality, speed, and reliability of data delivery.
• Independently diagnose and resolve complex production data issues, communicating risks, impact, and recommended solutions to technical and business stakeholders.

Requirements

• Strong experience building ETL/ELT pipelines and production-grade data processing.
• Proficiency in Python and SQL for transforming, validating, and serving large datasets.
• Experience designing and operating data platforms across AWS, Azure, Google Cloud, MS SQL, and PostgreSQL, including cloud and on-premise environments.
• Experience implementing data-quality automation: monitoring, alerting, anomaly detection, and remediation.
• Ability to lead architecture and engineering standards for pipelines, schemas, documentation, testing, deployment, observability, and version control.
• Experience with real-time or near-real-time ingestion using Kafka, Spark, and APIs.
• Knowledge of data governance practices including security, privacy, compliance, access controls, and data integrity.
• Technical leadership experience across solution design, code reviews, and troubleshooting.

Technology Stack

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