Sr. Data Analyst

Key Responsibilities:

• Design, implement, and maintain scalable data pipelines for the extraction, transformation, and loading (ETL) of large datasets
• Create algorithms and statistical models to extract actionable insights.
• Design, develop and implement data models for base tables and dashboards
• Conduct thorough analysis to understand data models, upstream source systems, and trends within datasets
• Query massive data sets to interpret complex relations
• Build complex logics for attributes, metrics and feature banks under engineered datasets and reports
• Leverage GenAI capabilities and machine learning techniques to enhance data analysis capabilities.
• Enable and/or develop AI use cases (ML or GenAI based)
• Test, vet, deploy, maintain and monitor AI pipelines
• Create visualizations and dashboards to communicate data-driven insights.
• Package and serve data products over the various reporting outlets in use
• Adhere to information security and personal data privacy mandates and guidelines in data collection, analysis, access management and reporting
• Implement best practices for data governance, quality and documentation
• Implement best practices and continuously improve data analytics processes
• Develop and maintain data architecture and data management, ensuring data integrity, security, and optimal performance

Education:

Bachelor's degree in IT, Computer Engineering, Computer Science, Data Science

Level of Experience:

Limited Experience (2-5Yrs) in a related field

Technical Skills & Knowledge:

Essential:

• Excellent knowledge of data warehousing and data modelling principle
• Excellent knowledge SQL
• Very good knowledge of Linux and OS Administration
• Excellent knowledge of Python
• Working knowledge of orchestration platforms (e.g. Airflow)

Desirable:

• Good knowledge of telecom core systems and data sets
• Good knowledge of key information security and networking principles
• Good knowledge of Scala
• Good knowledge of spark framework

Certifications & Licensure

Essential:

• SQL (any variant) Certification
• Python Certification
• Hadoop or Data Lakehouse Certification

Desirable:

• GenAI and LLM Engineering Certification
• Spark Certification
• Airflow Certification

Tools & Systems:

Essential:

• Data Warehousing or modern data platform
• Apache Hadoop eco-system
• Power BI (or other visualization tools)
• Python (base, pandas, scikit-learn)
• Agentic Development

Desirable:

• GenAI e2e solutions development