Data Analytics Engineer III

GCI Careers

6h•Hybrid

About The Position

GCI's Data Analytics Engineer III will be responsible for designing, developing, and maintaining interactive dashboards and reports that support data driven decision making within the organization. This hybrid role combines the functions of data engineering and analytics engineering, enabling the organization to leverage large-scale data for actionable business insights. Position will collaborate with cross-functional teams to ensure the quality, efficiency, and scalability of data while providing advanced analytics solutions to enhance network performance, customer experience, and operational efficiency.

Requirements

MS Office knowledge (e.g., Outlook, Teams, Word, Excel). Ability to Design, Evaluate, and test data infrastructure.
Proficiency in SQL, Python, and R for data manipulation and analytics.
Experience with big data technologies (e.g., Spark, Databricks) and cloud platforms (AWS, Azure, Google Cloud).
Proficient with data visualization tools (e.g., Tableau, Power BI).
Knowledge of machine learning algorithms and statistical analysis techniques.
Familiarity with telecommunications systems and related data types (e.g., network performance metrics, call data records).
Expertise in SQL, Python, and big data technologies (e.g., Databricks, Spark).
Strong background in statistics, machine learning, and data analytics, with the ability to apply these skills in a telecommunications context.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and data storage solutions (e.g., SQL, NoSQL databases).
Proven ability to lead projects and manage complex data analytics initiatives.
Experience with ETL processes, data pipeline design, and data warehousing.
Strong understanding of telecommunications data, network architecture, and key performance metrics.
Experience with spatial data analysis, map creation and geographic data visualization.
High School diploma or equivalent.
Bachelor’s degree in Computer Science, Software/Computer Engineering, or relevant field.
Minimum of six (6) years’ experience in data engineering, analytics engineering, or a related role in the telecommunications industry.

Nice To Haves

Experience with real-time data processing and streaming technologies (e.g., Kafka, Apache Flink).
Advanced degree (Master’s or PhD) in Data Science, Machine Learning, or a related field.
Familiarity with network optimization, customer experience analysis, or predictive analytics in telecommunications.
Other telecom industry or job specific certifications.

Responsibilities

Develop and implement data models, algorithms, and analytical solutions to derive insights from large datasets, including network performance analysis, customer behavior modeling, churn prediction, and operational optimization.
Implement AI driven workflows against data within analytical projects.
Create automated reporting and visualization tools (e.g., dashboards, KPI reports) to communicate insights to stakeholders and drive data-informed decision-making.
Collaborate with business units to understand analytical needs and translate them into actionable data solutions.
Design, develop, and maintain robust data pipelines that efficiently collect, process, and transform data from various telecommunications sources (e.g., network performance, customer usage data, call data records, billing systems).
Implement and manage ETL (Extract, Transform, Load) processes to ensure seamless integration of data from multiple systems into a centralized data warehouse or data lake.
Ensure data quality and integrity by identifying, resolving, and preventing data discrepancies and errors.
Optimize and streamline data storage and retrieval processes to support real-time and batch data analysis needs.
Work closely with data scientists, business analysts, and IT teams to design and implement visualizations that provide meaningful insights.
Provide technical guidance and support to junior team members and other departments in data-related initiatives.
Stay up to date with the latest trends and technologies in data engineering, analytics, and telecommunications.
Identify opportunities to improve existing data systems, pipelines, and analytics models to drive greater efficiency and business impact.
Contribute to the development and adoption of new data technologies, methodologies, and best practices within the organization.
Oversee the end-to-end analytics lifecycle including data modeling, data preparation and report development.
Integrate and manage big data technologies (e.g., Databricks, Spark, Kafka) for real-time data processing and analytics.
Build and maintain dashboards and report solutions to monitor network performance, customer behavior, and service quality.
Guide and mentor junior team members on data engineering best practices, tools, and techniques.
Work closely with other technical teams (e.g., network engineers, software engineers) to understand business requirements and translate them into data solutions.