Data Platform Engineer

Power International Holding

Job Overview

Location

Remote

Employment Type

Full-time

Work Arrangement

Remote

Sector

Energy & Utilities

Experience Level

Senior (5-8 years)

Application Deadline

July 14, 2026

About the Company

Power International Holding (PIH) is a prominent conglomerate with a diverse portfolio spanning multiple critical sectors. Their operations encompass Energy, Concessions & Construction, Healthcare, Services, Ventures, Contracting & Industries, Telecommunications, Real Estate, Lifestyle (Hospitality & Entertainment), and Agriculture & Food Industry. This broad reach positions PIH at the forefront of economic development and innovation. The company is committed to sustainable practices and actively engages in news and initiatives related to its various industry segments. Their focus on procurement and career development underscores a dedication to operational excellence and talent growth across their extensive business units.

Job Description

We are seeking a highly skilled Data Platform Engineer to join our dynamic team and play a crucial role in shaping our data infrastructure.

In this position, you will be instrumental in designing, building, and optimizing the enterprise data platform that underpins our analytics, AI, reporting, and automation initiatives. Your responsibilities will include developing scalable data pipelines, managing lakehouse and warehouse environments, and ensuring the reliability, security, and performance of our data systems.

This role is key to enabling robust data governance and supporting both real-time and batch data workloads for a wide range of business users, analytics experts, and AI teams.

To apply for this role, click the Apply button on this page and follow the instructions.

Required Skills

Data Platform ArchitectureData LakesLakehousesData WarehousesCloud Data EnvironmentsDatabricksSnowflakeBigQuerySynapseScalabilityReliabilityResilienceSecurityData PipelinesBatch IngestionStreaming IngestionStructured DataUnstructured DataETLELTData MovementERP IntegrationCRM IntegrationAPI IntegrationFlat File IntegrationOperational Systems IntegrationWorkflow AutomationDevOpsAirflowADFDagsterCI/CDAutomated TestingInfrastructure-as-CodeContainerizationData GovernanceSecurity EnablementAccess ControlsEncryptionMetadata ManagementLineage CaptureData CatalogingData DiscoveryComplianceData Quality MonitoringPerformance OptimizationWorkload UsageStorage EfficiencyQuery ResponsivenessCompute UtilizationPipeline ExecutionOperational CostsIncident TroubleshootingHigh AvailabilityBackupDisaster RecoveryRetentionRecovery ProceduresContinuity PlanningAnalytics SupportAI SupportAutomation SupportCurated DatasetsFeature-Ready DatasetsMachine LearningPredictive AnalyticsReal-time ConsumptionScheduled ConsumptionStakeholder CollaborationArchitectsAnalystsBusiness StakeholdersPlatform CapabilitiesNew TechnologiesEngineering PracticesSpeedValue DeliveryPlatform RoadmapsModernization InitiativesModern Data ArchitecturesMedallion ArchitectureData Engineering ToolsSQLPythonSparkDistributed ProcessingGovernance FoundationsPerformance TuningStakeholder Service MindsetData Engineering ExpertiseBig DataData WarehousingEnterprise PlatformsIntegrated Data EcosystemsCross Functional DeliveryEnterprise ReportingAgilityAI FluencyBig Data Analytics L3Build High-Performing TeamsDashboards L3Data Integration L4Deep Learning Algorithms L3LeadershipMachine Learning Trends And Techniques L3Provide DirectionQualityResilience

Key Responsibilities

  • Design, build, and maintain enterprise data platforms including data lakes, lakehouses, and data warehouses.
  • Manage cloud-based data environments such as Databricks, Snowflake, BigQuery, Synapse, or equivalent platforms.
  • Ensure scalability, reliability, resilience, and security of platform infrastructure across environments.
  • Build and maintain batch and streaming ingestion frameworks for structured and unstructured data sources.
  • Develop reusable ETL / ELT frameworks, templates, and standards for enterprise data movement.
  • Integrate data from ERP, CRM, APIs, flat files, operational systems, and streaming sources.
  • Implement orchestration tools such as Airflow, ADF, Dagster, or similar workflow schedulers.
  • Establish CI/CD pipelines for data engineering deployments and automated testing processes.
  • Promote infrastructure-as-code, containerization, and automation best practices.
  • Implement access controls, encryption, and platform security standards for enterprise data assets.
  • Enable metadata management, lineage capture, data cataloging, and discovery capabilities.
  • Support governance frameworks, compliance requirements, and data quality monitoring controls.
  • Monitor platform performance, workload usage, storage efficiency, and query responsiveness.
  • Optimize compute utilization, pipeline execution times, and operational costs.
  • Troubleshoot platform incidents and ensure high availability with minimal disruption.
  • Manage Dev, Test, and Production environments with controlled release practices.
  • Implement backup, disaster recovery, retention, and recovery procedures for critical data assets.
  • Ensure continuity planning for data platform operations and service resilience.
  • Deliver curated datasets and optimized data structures for reporting, dashboards, AI models, and automation use cases.
  • Enable feature-ready datasets for machine learning and predictive analytics workloads.
  • Support both real-time and scheduled consumption models for enterprise users.
  • Work closely with architects, analysts, AI teams, automation teams, and business stakeholders to improve platform capabilities.
  • Recommend new technologies and engineering practices that enhance speed, scalability, and value delivery.
  • Contribute to platform roadmaps, standards, and long-term modernization initiatives.

Qualifications

  • Bachelor's Degree in Information Technology or any related field.
  • Master's degree in Information Technology or any related field is a plus.
  • Minimum 5–10 years of experience in Data Engineering, Data Platform Engineering, Big Data, or Data Warehousing roles.
  • Proven experience building cloud data platforms, pipelines, and integrated data ecosystems.
  • Experience supporting analytics, AI, automation, and enterprise reporting functions.
  • Strong knowledge of modern data architectures: lakehouse, warehouse, medallion, and scalable data platform models.
  • Expertise in ETL / ELT, orchestration, SQL, Python, Spark, and distributed processing.
  • Understanding of governance foundations: metadata, lineage, catalogs, security, and quality frameworks.
  • Strong capability in tuning pipelines, queries, storage, and compute efficiency.
  • Ability to deliver reliable platform services to multiple business consumers.

How to Apply

Click the 'Apply now' button to submit your application.

Join Our Communities

The data landscape is rapidly evolving, with Qatar emerging as a hub for technological advancement in the energy sector. This role is pivotal in shaping that future by architecting and maintaining a robust enterprise data platform. You will leverage expertise in data warehousing, lakehouse architectures, and cloud-native solutions like Databricks or Snowflake. Your impact will directly influence business intelligence, AI model development, and operational automation, driving significant ROI through enhanced data accessibility and performance. This position requires a deep understanding of scalable data pipelines and integration frameworks.

Posted Date

June 30, 2026