Senior Data Engineer

- 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 databases. - Data modeling: Create clean, reusable data models for core entities such as customers, vehicles, drivers, stations, transactions, wallets, limits, invoices, products, maintenance services, and geographic coverage. - Reliability and quality: Implement data validation, lineage, observability, alerting, reconciliation, and automated quality checks to ensure business-critical dashboards and reports are accurate and timely. - Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts. - Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost. - Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle. - Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds. - DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations. - Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders. - Technical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at PetroApp. Requirements Required qualifications - 5+ years of professional experience in data engineering, analytics engineering, platform engineering, or backend engineering with strong data ownership. - Advanced SQL skills, including query optimization, data modeling, window functions, incremental transformations, and large-table performance tuning. - Strong Python programming experience for data pipelines, automation, testing, and production-grade data workflows. - Hands-on experience with workflow orchestration such as Airflow, Dagster, Prefect, or similar tools. - Experience with modern data warehouses or lakehouse platforms such as BigQuery, Snowflake, Redshift, Databricks, Delta Lake, Iceberg, or equivalent. - Experience building reliable ELT/ETL pipelines using tools such as dbt, Spark, Kafka, Flink, Fivetran, Stitch, custom API ingestion, or CDC frameworks. - Practical understanding of data quality, schema evolution, monitoring, alerting, backfills, idempotency, and failure recovery. - Experience designing dimensional, wide-table, and event-based data models for BI, analytics, and operational reporting. - Comfort working with cloud platforms such as AWS, GCP, or Azure, plus Git-based engineering workflows. - Strong communication skills with the ability to translate business requirements into clear technical designs and delivery plans. Preferred qualifications - Experience in fintech, payments, fleet management, logistics, mobility, marketplace, fuel, or high-volume transaction platforms. - Knowledge of event-driven architectures, streaming data, CDC, API integrations, data contracts, and data mesh or domain-oriented data ownership. - Experience supporting BI tools such as Power BI, Looker, Tableau, Metabase, Superset, or similar platforms. - Familiarity with MLOps or feature engineering for fraud detection, anomaly detection, forecasting, customer segmentation, or optimization use cases. - Experience with data privacy, access control, encryption, secrets management, and compliance expectations in the Middle East or multi-country operations. Core technical stack expectations The 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 processing. - Transformation and modeling: dbt or equivalent; dimensional modeling; metrics layers. - Orchestration: Airflow, Dagster, Prefect, or similar. - Storage and compute: cloud warehouse, data lake/lakehouse, object storage, distributed processing. - Streaming and integration: Kafka or equivalent, CDC, APIs, webhooks, files, partner data feeds. - Engineering practices: Git, CI/CD, automated tests, Docker, Kubernetes or containerized deployment, Terraform or infrastructure-as-code. - Observability: data quality checks, lineage, pipeline monitoring, logs, alerts, runbooks, and service-level objectives for data products. Benefits - Competitive salary and benefits package. - Opportunity to work on cutting-edge technology with a passionate team. - Career growth and development opportunities. - A collaborative and inclusive work environment.

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