Data platform engineering: Design and maintain scalable batch and near-real-time data pipelines across mobile applications, NFC/fuel transactions, station integrations, ERP integrations, payments, support systems, and operational databasesData modeling: Create clean, reusable data models for core entities such as customers, vehicles, drivers, stations, transactions, wallets, limits, invoices, products, maintenance services, and geographic coverageReliability and quality: Implement data validation, lineage, observability, alerting, reconciliation, and automated quality checks to ensure business-critical dashboards and reports are accurate and timelyAnalytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data martsPerformance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure costData governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycleIntegration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feedsDevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformationsIncident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholdersTechnical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at PetroAppRequirementsRequired qualifications5+ years of professional experience in data engineering, analytics engineering, platform engineering, or backend engineering with strong data ownershipAdvanced SQL skills, including query optimization, data modeling, window functions, incremental transformations, and large-table performance tuningStrong Python programming experience for data pipelines, automation, testing, and production-grade data workflowsHands-on experience with workflow orchestration such as Airflow, Dagster, Prefect, or similar toolsExperience with modern data warehouses or lakehouse platforms such as BigQuery, Snowflake, Redshift, Databricks, Delta Lake, Iceberg, or equivalentExperience building reliable ELT/ETL pipelines using tools such as dbt, Spark, Kafka, Flink, Fivetran, Stitch, custom API ingestion, or CDC frameworksPractical understanding of data quality, schema evolution, monitoring, alerting, backfills, idempotency, and failure recoveryExperience designing dimensional, wide-table, and event-based data models for BI, analytics, and operational reportingComfort working with cloud platforms such as AWS, GCP, or Azure, plus Git-based engineering workflowsStrong communication skills with the ability to translate business requirements into clear technical designs and delivery plansPreferred qualificationsExperience in fintech, payments, fleet management, logistics, mobility, marketplace, fuel, or high-volume transaction platformsKnowledge of event-driven architectures, streaming data, CDC, API integrations, data contracts, and data mesh or domain-oriented data ownershipExperience supporting BI tools such as Power BI, Looker, Tableau, Metabase, Superset, or similar platformsFamiliarity with MLOps or feature engineering for fraud detection, anomaly detection, forecasting, customer segmentation, or optimization use casesExperience with data privacy, access control, encryption, secrets management, and compliance expectations in the Middle East or multi-country operationsCore technical stack expectationsThe exact stack may evolve, but the successful candidate should be comfortable operating across the following categories:Languages: SQL, Python; optional Scala or Java for distributed processingTransformation and modeling: dbt or equivalent; dimensional modeling; metrics layersOrchestration: Airflow, Dagster, Prefect, or similarStorage and compute: cloud warehouse, data lake/lakehouse, object storage, distributed processingStreaming and integration: Kafka or equivalent, CDC, APIs, webhooks, files, partner data feedsEngineering practices: Git, CI/CD, automated tests, Docker, Kubernetes or containerized deployment, Terraform or infrastructure-as-codeObservability: data quality checks, lineage, pipeline monitoring, logs, alerts, runbooks, and service-level objectives for data productsBenefitsCompetitive salary and benefits packageOpportunity to work on cutting-edge technology with a passionate teamCareer growth and development opportunitiesA collaborative and inclusive work environment