Chevron operates through two subsidiaries in the Philippines. Our Downstream company is Chevron Philippines Inc. We also operate a business processes support organization through Chevron Holdings Inc.
There are two Chevron companies operating in the Philippines: Chevron Holdings Incorporated (CHI) and Chevron Philippines Incorporated (CPI).
CHI is a shared services center providing transactional, processing, and consulting services in the areas of finance and accounting, information technology, supply chain management, human resources, downstream customer service and marketing. Established in 1998, CHI serves Chevron affiliates in six continents around the world. Over the years, it has grown to be one of the leading members of the shared services industry in the Philippines.
CHI has received various recognitions as a top employer: the 2024 Circle of Excellence for CSR Company of the Year; 2024 Top 10 Fortune 500 companies; 2023 HR Asia best companies; 2022 Diversity Company of the Year; 2021 Asia’s Best Employer Brand Award; 2021 Global Best Employer Brand Award; 2020 HR Asia Best Companies to Work for in Asia; 2019 Circle of Excellence, Top Employer Category at Asia CEO Awards, and the 2018 Wellness Company of the Year at the same Asia CEO Awards.
CPI markets the Caltex brand of top-quality fuels, lubricants, and petroleum products through a network of service stations, terminals and sales offices.
About the Role
We have an exciting opportunity for Lead ERP Data Engineer. This role will be based in 6750 Building, Makati City, Philippines.
Responsibilities for this position may include but are not limited to
1. Azure Data Platform Engineering
Design, build, and enhance enterprise data platforms on Microsoft Azure, spanning ingestion, transformation, storage, orchestration, serving, and consumption layers.
Develop reusable data engineering frameworks and patterns supporting both SAP and non-SAP enterprise data sources.
Design scalable solutions using Azure Databricks, Azure Data Lake Storage Gen2, Azure Data Factory, Azure Synapse Analytics, SQL, and Microsoft Fabric, based on enterprise architecture standards.
Engineer batch and, where required, streaming or event-driven data pipelines.
Balance performance, scalability, resiliency, maintainability, and cloud cost when selecting data processing patterns.
Contribute to platform architecture standards, reusable components, engineering guardrails, and technology roadmaps.
2. SAP & Enterprise Data Integration
Design and develop scalable ingestion solutions for SAP S/4HANA, SAP BW/HANA and other relevant SAP platforms, alongside non-SAP sources.
Integrate enterprise data from platforms such as TM1, Salesforce, SQL databases, APIs, cloud applications, and flat/file-based sources.
Work with SAP functional and technical teams to understand SAP business objects, tables, CDS views, extractors, APIs, and business semantics.
Develop SAP-to-Azure ingestion and transformation patterns that preserve appropriate business context and relationships.
Implement incremental loading, change-data processing, reconciliation, restartability, and data lineage.
Optimize ingestion strategies for high-volume SAP datasets.
Partner with SAP Integration, Basis, Security, Functional, and Data teams to ensure appropriate extraction and consumption of SAP data.
3. Lakehouse & Medallion Architecture
Design and implement centralized ingestion and transformation frameworks aligned with Medallion Architecture principles.
Build and maintain
Bronze: raw, source-aligned and auditable data.
Silver: cleansed, standardized, validated and integrated data.
Gold: curated, business-ready datasets and analytical data products.
Design and implement Delta Lake-based solutions for scalable analytical processing.
Develop reusable frameworks for schema management, incremental processing, metadata management, and data transformations.
Define appropriate partitioning, optimization, retention, and lifecycle strategies.
4. Data Warehouse & Data Modeling
Design and optimize enterprise data warehouse and lakehouse schemas.
Develop dimensional data models using fact and dimension modeling, including appropriate star-schema patterns.
Design reusable analytical views, curated datasets, semantic-ready data products, and aggregation strategies.
Translate complex SAP transactional structures into business-consumable analytical models.
Optimize SQL queries, physical structures, processing logic, and data-access patterns.
Ensure models support enterprise BI, operational analytics, advanced analytics, and AI use cases.
5. Data Pipeline & Orchestration Engineering
Develop reliable, parameterized, reusable pipelines using Azure Data Factory, Azure Databricks, Synapse workflows, and/or Fabric Data Factory.
Implement workflow dependencies, scheduling, triggers, retries, exception handling, checkpoints, logging, and notifications.
Develop metadata-driven pipeline patterns to minimize duplicated engineering.
Build pipelines supporting both full and incremental data loads.
Establish operational monitoring and support procedures.
Troubleshoot pipeline failures, performance degradation, data-quality issues, and source-system changes.
6. AI & Data Engineering
Engineer and curate enterprise datasets for machine learning, Generative AI, and agentic AI applications.
Prepare structured and unstructured data for AI consumption, including cleansing, chunking, metadata enrichment, transformation, and appropriate access controls.
Build pipelines supporting RAG, embeddings, vectorization, feature engineering, model/agent grounding, and AI application data requirements.
Develop data services and APIs that securely expose governed enterprise and SAP data to AI applications.
Collaborate with AI Engineers and Data Scientists to productionize AI workloads.
Support ingestion and transformation of documents, logs, JSON/XML and other semi-structured or unstructured content.
Implement monitoring and data-quality controls specifically for AI datasets.
Support AI-driven data engineering approaches, such as metadata generation, anomaly detection, pipeline troubleshooting, code assistance, and automated quality validation.
About You
We know our greatest resource is the ingenuity, creativity and innovation of our people. Applications from motivated and talented individuals with a keen interest in the energy industry are encouraged.
Required Qualifications
10-15 years of experience in data engineering, data warehousing, cloud data platforms, business intelligence engineering, or related disciplines.
3–5+ years of hands-on Microsoft Azure data engineering experience in enterprise-scale environments.
Strong hands-on experience with Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics and/or Microsoft Fabric.
Advanced proficiency in SQL and strong experience developing complex transformations, stored procedures, views, data models, and analytical datasets.
Strong proficiency in Python and/or PySpark for scalable data transformation and processing.
Experience implementing lakehouse and Medallion Architecture patterns.
Demonstrated experience designing Bronze, Silver, and Gold data layers and reusable ingestion/transformation frameworks.
Experience designing data warehouse and dimensional models, including fact/dimension and star-schema approaches.
Experience ingesting and integrating data from SAP systems into Azure or equivalent cloud data platforms.
Working knowledge of SAP S/4HANA, SAP BW/HANA, or related SAP data structures and integration/extraction methods.
Experience integrating heterogeneous sources, including relational databases, APIs, files, cloud platforms, and enterprise applications.
Strong understanding of data quality, reconciliation, lineage, metadata, and governance concepts.
Experience implementing cloud security using RBAC, managed identities/service principals, secret management, and least-privilege principles.
Experience with Azure DevOps, Git and CI/CD for data engineering workloads.
Strong experience troubleshooting and optimizing large-scale data pipelines, Spark workloads, and SQL queries.
Strong analytical, engineering, communication, and stakeholder-management skills.
Bachelor's degree in Computer Science, Data Engineering, Information Technology, Engineering, Mathematics, or a related discipline, or equivalent relevant professional experience.
Preferred Qualifications
Advanced experience with Azure Databricks, including Delta Lake, Unity Catalog, Spark optimization and lakehouse engineering.
Experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, or related workloads.
Experience using Microsoft Purview or comparable enterprise data-governance platforms.
Experience building modern data solutions from SAP S/4HANA, SAP HANA, SAP BW, CDS Views, OData/APIs or other SAP extraction mechanisms.
Understanding of SAP business data across domains such as Finance, Procurement, Supply Chain, Logistics, Asset Management, or Master Data.
Experience building datasets and pipelines for machine learning and Generative AI workloads.
Knowledge of Microsoft Foundry, Azure AI services, and enterprise AI architectures.
Experience implementing RAG pipelines, embeddings, vector search, semantic retrieval, or AI grounding architectures.
Familiarity with MLOps/LLMOps, AI evaluation, model/agent monitoring and Responsible AI principles.
Experience with near-real-time or streaming technologies such as Azure Event Hubs, Kafka, or Databricks streaming capabilities.
Experience with Infrastructure as Code using Terraform or Bicep.
Experience building APIs or data services to expose enterprise data to applications and AI agents.
Relevant Microsoft, Databricks, or SAP certifications are advantageous.
Everything we do at Chevron is guided by our values and our commitment to The Chevron Way (https://www.chevron.com/about/the-chevron-way). At the heart of The Chevron Way is our vision to be the global energy company most admired for its people, partnership, and performance.
Participate in world-leading energy projects, advance your professional development and expand your career within an inclusive, collaborative and high-performing workplace.
Benefits
Competitive salary
Allowances, medical and optical reimbursements
Health care coverage for you and your eligible dependents
Robust employee centered programs for health and wellness
Time-off to promote healthy work-life balance or to care for your family
Annual corporate incentive bonus when the company meets established goals
Recognition & awards program
Life insurance
Career development opportunities to all employees through onboarding, training and development, mentoring, volunteering opportunities and employee networking groups
At Chevron, we are committed to fostering diversity and inclusion at all levels of our company and at all stages of the employee experience. We constantly strive to attract, develop and retain diverse Filipino talent. Globally, Chevron Corporation has achieved a rating of 100 percent in the Human Rights Campaign Equality Index for the past 17 years. The Index ranks American companies based on their commitment to equality in the workplace.
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