{"id":4193465,"url":"https://alion.io/job/chevron-data-engineer-sap","title":"Data Engineer - SAP","company":{"id":5944,"name":"Chevron","domain":"chevron.com","url":"https://alion.io/company/chevron","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":13,"ghost_share":0,"stale_share":0.077,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Enterprise Apps","role_family":"Enterprise Apps","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Makati, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CDS Views","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Least Privilege","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SAP BusinessObjects","optional":false},{"name":"SAP BW","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"ABAP","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Bicep","optional":true},{"name":"LLMOps","optional":true},{"name":"SAP HANA","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-11-12T20:00:18Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T20:00:18Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Total Number of Openings\n1The Data Engineer designs, develops, and operates scalable enterprise data solutions that integrate SAP and non-SAP data across the Microsoft Azure ecosystem. The role builds and optimizes ingestion frameworks, lakehouse and data warehouse solutions, transformation pipelines, data models, and governed data products that support enterprise reporting, analytics, AI, and intelligent applications.The role combines expertise in Azure data engineering, SAP data integration, lakehouse architecture, Databricks, SQL, DevOps, data governance, and AI engineering to create trusted, reusable, secure, and high-performing enterprise data foundations.\nAbout Us\nChevron is a leading multinational energy company with operations in over 180 countries. Founded in 1879, Chevron has a rich history of innovation and growth, making significant contributions to the global energy landscape.\nChevron markets Caltex® fuels, lubricants and other petroleum products in the Philippines. Our network of service stations, terminals and sales offices forms the backbone of our presence in the Philippines.\nIn March 2020, Chevron sold its 45% non-operated interest in the Malampaya natural gas field. This was the first natural gas development and largest industrial project in the Philippines.\nFrom the Philippines, we provide operational support for Chevron offices on five continents. The workforce supplies transactional, processing and consulting services in areas ranging from finance to information technology.\nChevron 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.\nThere are two Chevron companies operating in the Philippines: Chevron Holdings Incorporated (CHI) and Chevron Philippines Incorporated (CPI).\nCHI 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.\nCHI 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.\nCPI markets the Caltex brand of top-quality fuels, lubricants, and petroleum products through a network of service stations, terminals and sales offices.\nAbout the Role\nWe have an exciting opportunity for Lead ERP Data Engineer. This role will be based in 6750 Building, Makati City, Philippines.\nResponsibilities for this position may include but are not limited to:\n1. Azure Data Platform Engineering\nDesign, build, and enhance enterprise data platforms on Microsoft Azure, spanning ingestion, transformation, storage, orchestration, serving, and consumption layers. \nDevelop reusable data engineering frameworks and patterns supporting both SAP and non-SAP enterprise data sources. \nDesign 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. \nEngineer batch and, where required, streaming or event-driven data pipelines. \nBalance performance, scalability, resiliency, maintainability, and cloud cost when selecting data processing patterns. \nContribute to platform architecture standards, reusable components, engineering guardrails, and technology roadmaps. \n2. SAP & Enterprise Data Integration\nDesign and develop scalable ingestion solutions for SAP S/4HANA, SAP BW/HANA and other relevant SAP platforms, alongside non-SAP sources. \nIntegrate enterprise data from platforms such as TM1, Salesforce, SQL databases, APIs, cloud applications, and flat/file-based sources. \nWork with SAP functional and technical teams to understand SAP business objects, tables, CDS views, extractors, APIs, and business semantics. \nDevelop SAP-to-Azure ingestion and transformation patterns that preserve appropriate business context and relationships. \nImplement incremental loading, change-data processing, reconciliation, restartability, and data lineage. \nOptimize ingestion strategies for high-volume SAP datasets. \nPartner with SAP Integration, Basis, Security, Functional, and Data teams to ensure appropriate extraction and consumption of SAP data. \n3. Lakehouse & Medallion Architecture\nDesign and implement centralized ingestion and transformation frameworks aligned with Medallion Architecture principles. \nBuild and maintain: \nBronze: raw, source-aligned and auditable data. \nSilver: cleansed, standardized, validated and integrated data. \nGold: curated, business-ready datasets and analytical data products. \nDesign and implement Delta Lake-based solutions for scalable analytical processing. \nDevelop reusable frameworks for schema management, incremental processing, metadata management, and data transformations. \nDefine appropriate partitioning, optimization, retention, and lifecycle strategies. \n4. Data Warehouse & Data Modeling\nDesign and optimize enterprise data warehouse and lakehouse schemas. \nDevelop dimensional data models using fact and dimension modeling, including appropriate star-schema patterns. \nDesign reusable analytical views, curated datasets, semantic-ready data products, and aggregation strategies. \nTranslate complex SAP transactional structures into business-consumable analytical models. \nOptimize SQL queries, physical structures, processing logic, and data-access patterns. \nEnsure models support enterprise BI, operational analytics, advanced analytics, and AI use cases. \n5. Data Pipeline & Orchestration Engineering\nDevelop reliable, parameterized, reusable pipelines using Azure Data Factory, Azure Databricks, Synapse workflows, and/or Fabric Data Factory. \nImplement workflow dependencies, scheduling, triggers, retries, exception handling, checkpoints, logging, and notifications. \nDevelop metadata-driven pipeline patterns to minimize duplicated engineering. \nBuild pipelines supporting both full and incremental data loads. \nEstablish operational monitoring and support procedures. \nTroubleshoot pipeline failures, performance degradation, data-quality issues, and source-system changes. \n6. AI & Data Engineering\nEngineer and curate enterprise datasets for machine learning, Generative AI, and agentic AI applications. \nPrepare structured and unstructured data for AI consumption, including cleansing, chunking, metadata enrichment, transformation, and appropriate access controls. \nBuild pipelines supporting RAG, embeddings, vectorization, feature engineering, model/agent grounding, and AI application data requirements. \nDevelop data services and APIs that securely expose governed enterprise and SAP data to AI applications. \nCollaborate with AI Engineers and Data Scientists to productionize AI workloads. \nSupport ingestion and transformation of documents, logs, JSON/XML and other semi-structured or unstructured content. \nImplement monitoring and data-quality controls specifically for AI datasets. \nSupport AI-driven data engineering approaches, such as metadata generation, anomaly detection, pipeline troubleshooting, code assistance, and automated quality validation. \nAbout You\nWe 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.\nRequired Qualifications\n10-15 years of experience in data engineering, data warehousing, cloud data platforms, business intelligence engineering, or related disciplines. \n3-5+ years of hands-on Microsoft Azure data engineering experience in enterprise-scale environments. \nStrong hands-on experience with Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics and/or Microsoft Fabric. \nAdvanced proficiency in SQL and strong experience developing complex transformations, stored procedures, views, data models, and analytical datasets. \nStrong proficiency in Python and/or PySpark for scalable data transformation and processing. \nExperience implementing lakehouse and Medallion Architecture patterns. \nDemonstrated experience designing Bronze, Silver, and Gold data layers and reusable ingestion/transformation frameworks. \nExperience designing data warehouse and dimensional models, including fact/dimension and star-schema approaches. \nExperience ingesting and integrating data from SAP systems into Azure or equivalent cloud data platforms. \nWorking knowledge of SAP S/4HANA, SAP BW/HANA, or related SAP data structures and integration/extraction methods. \nExperience integrating heterogeneous sources, including relational databases, APIs, files, cloud platforms, and enterprise applications. \nStrong understanding of data quality, reconciliation, lineage, metadata, and governance concepts. \nExperience implementing cloud security using RBAC, managed identities/service principals, secret management, and least-privilege principles. \nExperience with Azure DevOps, Git and CI/CD for data engineering workloads. \nStrong experience troubleshooting and optimizing large-scale data pipelines, Spark workloads, and SQL queries. \nStrong analytical, engineering, communication, and stakeholder-management skills. \nBachelor's degree in Computer Science, Data Engineering, Information Technology, Engineering, Mathematics, or a related discipline, or equivalent relevant professional experience. \nPreferred Qualifications\nAdvanced experience with Azure Databricks, including Delta Lake, Unity Catalog, Spark optimization and lakehouse engineering.\nExperience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, or related workloads.\nExperience using Microsoft Purview or comparable enterprise data-governance platforms.\nExperience building modern data solutions from SAP S/4HANA, SAP HANA, SAP BW, CDS Views, OData/APIs or other SAP extraction mechanisms.\nUnderstanding of SAP business data across domains such as Finance, Procurement, Supply Chain, Logistics, Asset Management, or Master Data.\nExperience building datasets and pipelines for machine learning and Generative AI workloads.\nKnowledge of Microsoft Foundry, Azure AI services, and enterprise AI architectures.\nExperience implementing RAG pipelines, embeddings, vector search, semantic retrieval, or AI grounding architectures.\nFamiliarity with MLOps/LLMOps, AI evaluation, model/agent monitoring and Responsible AI principles.\nExperience with near-real-time or streaming technologies such as Azure Event Hubs, Kafka, or Databricks streaming capabilities.\nExperience with Infrastructure as Code using Terraform or Bicep.\nExperience building APIs or data services to expose enterprise data to applications and AI agents.\nRelevant Microsoft, Databricks, or SAP certifications are advantageous.\nEverything 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.\nParticipate in world-leading energy projects, advance your professional development and expand your career within an inclusive, collaborative and high-performing workplace.\nBenefits:\nCompetitive salary\nAllowances, medical and optical reimbursements\nHealth care coverage for you and your eligible dependents\nRobust employee centered programs for health...","description_format":"text","description_chars":12938,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Life insurance","Professional development"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Fossil Fuels","Chemical Manufacturing","Alternative Fuels","Oil & Gas"],"lifecycle":[{"event":"open","at":"2026-11-12T20:00:18Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":18,"reasons":["conf:1","win:early","comp:brand"],"computed_at":"2026-11-12T21:29:05Z"},"pay":null,"html_url":"https://alion.io/job/chevron-data-engineer-sap","json_url":"https://alion.io/job/chevron-data-engineer-sap.json","meta":{"generated_at":"2026-11-12T21:29:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":45409,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5944},"rest":"https://alion.io/mcp/rest/get_company?id=5944"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fchevron-data-engineer-sap"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"free","price":{"credits":0,"usd":0},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fchevron-data-engineer-sap"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fchevron-data-engineer-sap"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/chevron-data-engineer-sap\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fchevron-data-engineer-sap"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4193465}},{"tool":"search_jobs","arguments":{"company":"chevron.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4193465},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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