Job description
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.
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.
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.
