Senior Data Engineer

Responsibilities:

• Data Pipeline Development and Management: Design, construct, install, test, and maintain highly scalable data management systems. Develop and optimize ETL/ELT pipelines using PySpark and Databricks to process large volumes of structured and unstructured data.
• Cloud Infrastructure: Utilize AWS services for data storage, computation, and orchestration, ensuring a reliable and efficient data infrastructure.
• Data Analysis and Insights: Collaborate with business stakeholders to understand customer experience challenges and opportunities. Analyze complex datasets to identify trends, patterns, and insights related to customer behavior, network performance, product usage, and churn.
• Business Use Case Analysis: Apply candidates analytical skills to various customer experience use cases, including:
• Churn Prediction: Develop models to identify customers at risk of leaving and understand the underlying drivers.
• Network Experience: Analyze network performance data to identify and address areas of poor customer experience.
• Personalization: Enable data-driven personalization of marketing communications, offers, and customer support interactions.
• Billing and Service Inquiries: Analyze inquiry data to identify root causes of customer confusion and drive improvements in billing and service clarity.
• Reporting and Visualization: Create compelling and insightful reports and dashboards using Tableau or Power BI to communicate findings to both technical and non-technical audiences.
• Data Governance and Quality: Ensure data accuracy, completeness, and consistency across all data platforms. Implement data quality checks and best practices.
• Collaboration and Mentorship: Work closely with cross-functional teams, including product, marketing, and engineering, to deliver data-driven solutions. Mentor junior team members and promote a culture of data-driven decision-making.

Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.
• Experience: 5+ years of experience in a data engineering or data analyst role, with a proven track record of working with large-scale data ecosystems.
• Technical Skills:
• Expert-level proficiency in Python and SQL.
• Hands-on experience with PySpark for big data processing.
• In-depth knowledge of the Databricks platform.
• Strong experience with AWS cloud services (e.g., S3, EC2, Redshift, EMR).
• Demonstrated expertise in data visualization and reporting with Tableau or Power BI