Senior Data Engineer

Senior Data Engineer role focused on building scalable data infrastructure and business logic for analytics, reporting, and network and 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 Python and SQL to process, transform, validate, and serve large and complex datasets.
• Lead implementation of automated data-quality controls, including anomaly detection, monitoring, alerting, and remediation workflows.
• 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 and reliability risks; 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 requirements into scalable data solutions.
• Provide technical leadership across data initiatives, including solution design, architecture decisions, code reviews, troubleshooting, and guidance to less-experienced engineers or technical partners.
• Define and maintain trusted datasets and data products 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 tooling.
• 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; communicate risks, impacts, and recommended solutions to technical and business stakeholders.

Requirements

• Experience designing and optimizing scalable ETL/ELT pipelines integrating enterprise data sources (CRM, OSS/BSS, GIS, network monitoring tools, REST APIs).
• Strong Python and SQL skills for processing, transforming, validating, and serving large datasets.
• Proficiency with data models, data lakes, and data warehouses to support BI, analytics, and operational use cases.
• Experience with data infrastructure across AWS, Azure, and Google Cloud, plus MS SQL and PostgreSQL.
• Hands-on experience with data quality automation, monitoring, anomaly detection, alerting, and remediation.
• Experience with performance evaluation for pipelines/platforms and improving scalability and reliability.
• Knowledge of security, privacy, compliance, access controls, and data governance for data solutions.
• Experience with data lineage, metadata, documentation, and data-integrity practices.
• Experience supporting real-time or near-real-time data integrations using Kafka, Spark, and APIs.

Technology Stack

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