<ol><li>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.</li><li>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.</li><li>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.</li><li>Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts.</li><li>Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost.</li><li>Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle.</li><li>Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds.</li><li>DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations.</li><li>Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders.</li><li>Technical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at <a href="https://himalayas.app/companies/petroapp">PetroApp</a>.</li></ol><h3>Requirements</h3><h3>Required qualifications</h3><ul><li>5+ years of professional experience in data engineering, analytics engineering, platform engineering, or backend engineering with strong data ownership.</li><li>Advanced SQL skills, including query optimization, data modeling, window functions, incremental transformations, and large-table performance tuning.</li><li>Strong Python programming experience for data pipelines, automation, testing, and production-grade data workflows.</li><li>Hands-on experience with workflow orchestration such as Airflow, Dagster, Prefect, or similar tools.</li><li>Experience with modern data warehouses or lakehouse platforms such as BigQuery, Snowflake, Redshift, Databricks, Delta Lake, Iceberg, or equivalent.</li><li>Experience building reliable ELT/ETL pipelines using tools such as dbt, Spark, Kafka, Flink, Fivetran, Stitch, custom API ingestion, or CDC frameworks.</li><li>Practical understanding of data quality, schema evolution, monitoring, alerting, backfills, i