Data Engineer

Role Purpose:

Vodafone is Oman's latest Network Operator. The presence of Vodafone in Oman will contribute to the development of all sectors, including the economic and educational sectors, and in line with Oman Vision 2040.

Our purpose is to 'Connect for a Better Future', which underpins everything we do. Our solutions will positively impact society as we connect everyone in Oman for a better future. We offer market-leading customer experiences and innovative digital services.

We are seeking energetic self-starters to work in this 100% Omani owned, positive, collaborative start-up environment, where key talents will create Oman's happiest workplace. We value individuals with an entrepreneurial mindset who thrive on challenges and change, and who want to make a positive impact on the lives of our customers and the people of Oman.

As part of our Strategy and Planning team, we are looking for a Data Engineer. In this role, you will uncover hidden patterns, trends, and opportunities in large datasets using tools like Hive, Spark, and SQL. Your goal is to translate data into actionable insights, optimize decision-making, and identify high-value opportunities across the organization.

Key accountabilities and decision ownership:

• Perform deep-dive analyses on large-scale datasets stored in Hadoop/HDFS, Hive, or cloud platforms.
• Use Spark (PySpark/SQL) for efficient data aggregation, transformation, and exploratory analysis.
• Develop statistical models, segmentation strategies, and predictive analytics to solve business problems.
• Identify trends, anomalies, and correlations to inform strategic initiatives (e.g., customer behavior, operational efficiency).
• Design and execute A/B tests, cohort analyses, and scenario modeling to validate hypotheses.
• Build interactive dashboards (Apache Superset) to democratize insights across teams.
• Partner with business leaders to define KPIs, metrics, and analytical requirements.
• Translate technical findings into clear, actionable recommendations for non-technical audiences.
• Prioritize analytics projects based on potential business value and ROI.

Core competencies, knowledge, and experience:

• Strong ability to analyze complex data, identify trends and patterns, and solve intricate business problems using data-driven approaches.
• Proven experience in leading and managing a team of data professionals, with the ability to mentor and foster collaboration.
• Excellent communication skills to translate complex analytical concepts into understandable language for non-technical stakeholders.
• Ability to develop and implement data strategies aligned with organizational goals and objectives.
• Strong technical skills in data science, including programming (Python, R), database management (SQL), and familiarity with big data technologies (Hadoop, Spark).
• In-depth knowledge of statistical analysis, predictive modeling, machine learning algorithms, and their application to business problems.
• Experience in data governance, data quality management, and ensuring data integrity and security.
• Understanding of industry and business operations to align data analytics strategies with business objectives.
• Familiarity with the latest data analytics tools and platforms, including cloud solutions, data visualization tools (Apache Superset), and advanced analytics software.

Key Job indicators:

• Accuracy and impact of analytical models and evaluation of data model precision.
• Number of innovative solutions or models developed to enhance data capabilities.
• Adherence to data privacy laws and internal governance standards.
• Feedback scores from stakeholders on the effectiveness of data insights and support.

Must have technical/professional qualifications:

• Minimum 3-5 years of experience in data analytics, with expertise in analyzing large datasets.
• Proficiency in SQL and experience with Hive in Hadoop ecosystems.
• Strong skills in Python or R for analysis, visualization (Matplotlib, Seaborn), and scripting.
• Hands-on experience with Spark (PySpark/Spark SQL) for big data processing.
• Familiarity with workflow tools like Airflow.
• Ability to create compelling data stories using tools like Apache Superset.
• C# and .NET knowledge is an advantage.