• Define and execute the data platform strategy, including architecture, tools, and data integration approaches. • Lead the design and evolution of scalable data infrastructure leveraging tools such as Databricks and Fivetran. • Establish standards for data ingestion, transformation, storage, and access across the organization. • Align data platform capabilities with business priorities and long-term analytics objectives. • Oversee the development and maintenance of data pipelines, ETL/ELT processes, and data models. • Provide technical leadership to Data Engineers in building efficient, reliable, and scalable data solutions. • Ensure seamless integration of data from systems such as Oracle Fusion into centralized data environments. • Partner with Report Analysts and business teams to ensure data availability supports reporting and analytics needs. • Establish and enforce data governance policies, standards, and best practices.

Role 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. 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. Requirements - 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 - 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.

**What You Will Do ❓** - Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt to power analytics and business-critical applications. - Develop and optimize data models following Medallion Architecture (Bronze, Silver, Gold) to create reliable, reusable, and high-quality datasets. - Build and maintain cloud-native data platforms using S3, Spark, Trino, and BigQuery, ensuring scalability, reliability, and cost efficiency. - Design robust data ingestion frameworks leveraging CDC (Maxwell), Kafka, and event-driven architectures to support near real-time data processing. - Create, optimize, and maintain data warehouses and data marts that enable fast, reliable reporting and self-service analytics. - Partner closely with Product Managers, Data Analysts, Backend Engineers, and Business stakeholders to translate business requirements into scalable data solutions. - Develop reusable dbt models, testing frameworks, and documentation to improve data quality, governance, and developer productivity. - Optimize Spark jobs, Trino queries, and storage layouts for performance, reliability, and cost efficiency. - Own the end-to-end lifecycle of critical data pipelines, ensuring high availability, monitoring, SLA adherence, and proactive incident resolution. - Build and enhance the core data platform by developing reusable frameworks, automation, CI/CD pipelines, and engineering best practices. - Ensure data quality through validation, monitoring, lineage, and observability while implementing best practices for security and governance. - Enable analytics teams by delivering trusted datasets, semantic models, and dashboards that power decision-making through Metabase.

Role Description We're looking for a Finance Engineer — someone who can turn manual finance and accounting processes into scalable, automated, AI-supported workflows. This role sits at the intersection of Finance, Accounting, Systems, Automation, and AI. - Build and improve workflows for reporting, forecasting, month-end review, reconciliations, and financial controls. - Work directly with clients: understand their finance and accounting needs, configure tools, support vendor onboarding, and own system improvements end-to-end. - Translate finance and accounting needs into system logic, automations, workflows, and documentation. - Automate repetitive finance work using AI tools, low-code tools, integrations, and internal workflows. - Connect and structure data from accounting systems, CRMs, payment tools, spreadsheets, BI tools, and data warehouses. - Build checks, controls, and validation logic to improve accuracy and reduce manual errors. - Partner with our engineering team on APIs, integrations, data pipelines, and workflow orchestration. Qualifications - 5+ years of experience in finance systems, finance operations, automation, or engineering roles within complex multi-stakeholder or enterprise environments. - 1+ year of hands-on experience building AI-powered automations, agents, prompts, workflows, or internal tools (Claude / Claude Code, Cursor, Copilot, custom LLM builds, or similar). - Experience with APIs, system integrations, data warehouses, and automation tools. - Strong understanding of finance and accounting processes: month-end close, reconciliations, AP / procure-to-pay, revenue, reporting, and corporate finance. - Ability to understand how financial data flows between departments and systems. - Fluent English (C1+) and excellent client-facing communication skills — you'll work directly with customers' founders and finance teams. - Strong drive to eliminate repetitive manual work. - Proactivity and high level of responsibility. - Ability to work in a fast-paced environment. Requirements - Python or other scripting experience. - Experience administering financial, accounting, billing, or ERP systems. - Experience with tools like NetSuite, QuickBooks, Xero, Stripe, Salesforce, HubSpot, Zuora, BigQuery, Snowflake, PostgreSQL, Looker, Power BI, Tableau, Zapier, Make, n8n, or Workato. - Proven impact in numbers: hours saved, manual work reduced, faster reporting, or fewer errors. - Experience working with US-based clients or startups. Recruitment Process - Soft skills interview with the recruiter. - Hard skills interview with the Head of FP&A. - Test task. - Culture fit interview with the VP of Operations. Benefits - Build the future of finance work — design AI-native finance operations, not just maintain them. - Rapid skills improvement (you'll work with multiple client businesses with different models and challenges). - Growth opportunities according to our seniority grading (it comes with a compensation increase). - Flexible schedule and time-off policy. - 18 days of paid vacation per year, paid sick leaves.