Role Description Impact You Will Make in the Role: - Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response across all production data flows. - Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders. - Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs. - Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions. - Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one. - Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments. - Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns. - Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data. - Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics. - Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers. - Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem. - Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones. - Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios. - Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery. Qualifications - 4+ years of data engineering experience. - At least 2 years of experience with Databricks or the Apache Spark ecosystem across Azure and/or AWS. - Proficiency in PySpark, SQL, and Python with a strong track record of building and operating production-grade pipelines under SLA constraints. - Hands-on experience with Delta Lake, including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns. - Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments. - Solid working knowledge of PostgreSQL, including query optimization, schema design, and use as a source or sink in production data pipelines. - Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production. - Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy, including navigating tools and platforms that default to single-tenant assumptions. - Proven ability to work collaboratively across data science, product, and infrastructure teams, owning end-to-end delivery in a cross-functional environment. - Strong understanding of data governance, security, and compliance principles, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments. Preferred Qualifications / Experience - Experience operating Databricks workspaces across both Azure and AWS, including cost governance, cluster management, and cross-cloud data access. - Experience optimizing Databricks workloads in a Serverless environment, including compute cost governance and performance tuning for serverless compute. - Experience with Microsoft SQL Server in a data engineering or ETL context. - Exposure to ML feature engineering or feature stores (Databricks Feature Store, Feast, or similar) supporting predictive analytics. - Experience with customer onboarding automation or Infrastructure as Code (IaC) patterns for provisioning tenant data pipelines at scale. - Databricks Certified Data Engineer Associate or Professional certification.

• 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.