Data Engineer

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. What we offer are market-leading customer experiences and innovative digital services.

We are looking for 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 are looking for individuals with an entrepreneurial mindset who get excited by challenges and constant change and truly want to positively impact the lives of our customers and the people of Oman.

The Data Engineer is a hybrid data professional with two equally important areas of responsibility. The role acts as Vodafone Oman’s day-to-day technical owner for the vendor-led implementation and operation of the enterprise lakehouse, assuring architecture, engineering quality, delivery, security, reliability, documentation, and knowledge transfer. In parallel, the role uses enterprise data to conduct analysis, generate actionable insights, develop analytical data products, and support evidence-based commercial and operational decisions. The role bridges data platform engineering and applied analytics, ensuring that the lakehouse is both technically robust and actively used to create measurable business value., Lakehouse and vendor oversight

• Act as the day-to-day technical focal point between Vodafone Oman, the lakehouse implementation vendor, source-system owners, cloud and security teams, analysts, and data scientists.
• Translate business, analytics, governance, security, and operational needs into technical requirements, prioritised use cases, data contracts, acceptance criteria, and delivery plans.
• Guide and challenge the vendor on architecture, ingestion, medallion layers, data models, orchestration, quality, metadata, lineage, security, observability, resilience, and support arrangements
• Review vendor designs, code, configurations, test evidence, documentation, deployment plans, milestones, risks, issues, and service performance; drive corrective action where required.
• Coordinate data-source onboarding, access, testing, acceptance, release, cutover, incident resolution, operational readiness, handover, and knowledge transfer
• Monitor platform and pipeline health, data freshness, quality, reconciliation, performance, cost, security, and compliance with Vodafone standards.

Data analysis and insights

• Work with business stakeholders to frame analytical questions, define success measures, identify required data, and prioritise high-value insight use cases
• Extract, combine, validate, and analyse large datasets using SQL, Python, Spark, and appropriate statistical techniques.
• Identify trends, drivers, anomalies, customer behaviours, performance gaps, and commercial or operational opportunities
• Develop repeatable analytical datasets, KPI logic, dashboards, reports, and self-service data products using governed lakehouse data
• Apply descriptive, diagnostic, and selected predictive methods where appropriate, including segmentation, forecasting, propensity analysis, and experiment evaluation
• Communicate findings through clear visualisations, concise narratives, and actionable recommendations; track adoption and measurable business outcomes, * Vendor milestones and technical deliverables completed and accepted against agreed requirements, quality gates, and operational-readiness criteria.
• Lakehouse and pipeline availability, data freshness, completeness, accuracy, reconciliation, security, performance, and service-level compliance.
• Timely resolution of vendor delivery gaps and production incidents, supported by verified root causes and permanent fixes.
• Completeness of architecture records, data contracts, lineage, runbooks, documentation, handover, and internal knowledge transfer.
• Timeliness, accuracy, reproducibility, and stakeholder acceptance of analyses, dashboards, KPIs, and analytical data products
• Quality and clarity of insights, recommendations, and data storytelling delivered to decision-makers
• Adoption of analytical outputs and demonstrable commercial, customer, or operational value from implemented recommendations

• Strong data-engineering and lakehouse fundamentals, with sufficient hands-on depth to review designs, code, configurations, tests, and production issues
• Experience governing technology vendors or system integrators and assuring technical quality, delivery, documentation, support, and knowledge transfer
• Advanced SQL and strong Python skills for data preparation, exploratory analysis, automation, statistical analysis, and validation.
• Strong understanding of Spark, cloud storage, orchestration, lakehouse architecture, dimensional modelling, metadata, lineage, data quality, security, and observability.
• Ability to define business questions, analytical approaches, KPI logic, data requirements, acceptance criteria, and actionable recommendations.
• Experience with descriptive and diagnostic analytics, visualisation, segmentation, forecasting, experiment evaluation, and selected predictive techniques.
• Strong critical thinking, data storytelling, stakeholder engagement, negotiation, documentation, and escalation skills.
• Ability to manage concurrent platform-delivery and insight-generation priorities while maintaining quality and traceability, * Bachelor’s degree in Computer Science, Data Engineering, Data Science, Statistics, Information Systems, or a related quantitative discipline.
• Minimum 4-6 years of relevant experience spanning data engineering, data analysis, or data science, including technical oversight of a vendor-delivered data platform or lakehouse.
• Advanced SQL and strong Python skills, including data manipulation, analysis, visualisation, validation, and automation
• Strong knowledge of Spark, cloud object storage, orchestration, open lakehouse table formats, medallion architecture, data modelling, and distributed processing
• Practical knowledge of statistics, KPI design, exploratory analysis, segmentation, forecasting, experiment evaluation, and predictive modelling concepts
• Experience with a recognised business-intelligence or visualisation platform and presenting insights to business stakeholders
• Experience reviewing technical designs, test evidence, data-quality controls, security, monitoring, documentation, vendor deliverables, defects, cutover, and handover
• Relevant cloud data-platform, analytics, or architecture certification and telecommunications data experience are preferred