Data Engineer

• Design, build and maintain scalable data pipelines using modern data engineering tools (e.g., dbt, Airflow, Prefect).
• Develop and maintain dimensional and semantic data models to support analytics and operational use cases.
• Build curated data products that support analytics and modern AI workloads.
• Design data schemas and storage strategies optimized for both analytical and AI workloads.
• Build and maintain version control workflows and isolated development/test environments.
• Manage and optimize cloud-based data infrastructure ensuring performance, scalability, reliability and cost efficiency.
• Contribute to the architecture and evolution of the organization's modern data platform.
• Collaborate closely with AI, Analytics, and Product teams to deliver scalable, high-quality data solutions.

Senior Data Engineer

• Invest in our "Data Foundation" space to become a data-driven organization
• Provide our business with needed insights about our customers, performance, and future predictions
• Responsible for building the future enterprise Data Lake for Unifonic
• Develop end-to-end data-related capabilities from data ingestion to reporting
• Help establish industry best practices for the data engineering team
• Build the needed capabilities for the organization to become more data-driven
• Evolve our data architecture by determining structural requirements
• Develop and install solutions
• Test and maintain those databases to ensure their longevity and overall efficiency

• 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.

• Design, develop, and deploy AI and machine learning solutions that address business needs.
• Build and implement Agentic AI applications using modern AI frameworks.
• Lead the full machine learning lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.
• Optimize models to improve accuracy, scalability, and performance in production environments.
• Implement model monitoring, drift detection, and retraining strategies.
• Design scalable data pipelines to support machine learning workloads.
• Collaborate with data engineers to ensure reliable data ingestion, transformation, and storage.
• Develop and maintain ML-focused CI/CD pipelines.
• Implement model versioning, automated testing, and deployment best practices.
• Support containerized and cloud-native ML deployments.
• Design AI and ML solutions using cloud platforms such as Azure, AWS, or GCP.
• Architect scalable infrastructure to support enterprise AI applications.
• Recommend technology solutions aligned with business and technical requirements.
• Ensure AI solutions meet security, reliability, and performance standards.
• Partner with business stakeholders to understand objectives and translate them into technical solutions.
• Collaborate with cross-functional teams throughout the solution lifecycle.
• Mentor junior data scientists and ML engineers.
• Promote best practices in machine learning, software engineering, and MLOps.
• Stay informed about emerging AI technologies and industry trends.

Senior Data Scientist – AI & ML Engineer

• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and design appropriate solutions.
• Work closely with IT and infrastructure teams to deploy and maintain data storage and processing systems, including databases, data warehouses, and cloud infrastructure.
• Design, build, and maintain robust, scalable data pipelines to ingest, process, and transform data from various sources, including internal databases and external APIs.
• Optimize data pipelines for performance, scalability, and reliability, ensuring timely and accurate delivery of data.
• Implement data validation and quality checks to identify and rectify any issues in data pipelines, ensuring data integrity.
• Explore and evaluate new technologies, tools, and frameworks to improve data engineering processes and capabilities.
• Perform data modeling and schema design to support analytical and reporting requirements.
• Design and build data marts and business layers to facilitate efficient data access and analysis.

• Design and build extraction, transformation, and loading (ETL) pipelines using Microsoft Fabric (Dataflow Gen2, Notebooks, or equivalent tools)
• Write optimized SQL queries and transformations for data ingestion from designated source systems
• Apply data quality rules and validation logic at each pipeline stage
• Implement incremental loads and manage refresh schedules for performance
• Escalate to Lead for architectural decisions or complex transformation patterns
• Define and implement data quality checks at ingestion, transformation, and output stages
• Perform ongoing data validation to ensure pipeline outputs align with business logic and source system expectations
• Identify, document, and escalate data quality issues with root cause analysis
• Maintain data quality dashboards and SLA monitoring
• Support UAT for new data sources or transformation logic
• Build and maintain data transformations using Power Query, SQL, or Python as appropriate
• Develop dimensional models and define aggregation logic aligned with analytics requirements
• Optimize data structures for performance and maintainability
• Document transformation logic, lineage, and assumptions per team standards
• Troubleshoot pipeline failures and performance issues; coordinate resolution with IT/Engineering
• Respond to data discrepancy reports from business users and analysts
• Maintain documentation of data sources, data dictionaries, and transformation specifications
• Support capacity planning and optimization of Fabric environments and pipelines
• Collaborate with Lead to define semantic models and calculated metrics

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