# Senior Data Engineer

> PetroApp · Egypt (Remote) · Full-time · Posted 2026-07-08

**Workplace:** remote

**Department:** Tech

## Description

1.  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.
2.  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.
3.  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.
4.  Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts.
5.  Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost.
6.  Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle.
7.  Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds.
8.  DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations.
9.  Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders.
10.  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.

## Apply

[Apply at PetroApp](https://apply.workable.com/petroapp/j/D93955A9AF/apply)

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