{"id":1006038,"url":"https://alion.io/job/procter-gamble-data-platform-engineer-rd","title":"Data Platform Engineer, R&D","company":{"id":871,"name":"Procter & Gamble","domain":"pg.com","url":"https://alion.io/company/pgcareers","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":115,"ghost_share":0,"stale_share":0.678,"repost_share":0.026,"time_to_fill_p50_days":45,"computed_at":"2026-11-11T05:49:22Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":46000,"max_usd":114000,"period":"year","method":"role_country_seniority_unknown","sample_n":77},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Knowledge Graph","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-17T16:26:37Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-11T03:52:12Z","board_verified":true,"closed_at":null,"days_open":54,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":54},"description":"Job Location\nSINGAPORE TC-BIOPOLISJob Description\nOverview of role\nYou will have the opportunity to build and operatethe scalable scientific data foundation connecting consumer, biological, OMICS, assay, formulation, and performance data. You will be responsible to deliver reliable, traceable, and scalablepipelines and services that reduce manual handling and provide trusted data for analytics, computational biology, predictive modeling, and AI-supported discovery.You will own data pipelines, interfaces, technical controls, data availability, reliability, operational support, and scalability. If this sounds exciting to you, read on!\nKey Responsibilities\n1. Engineer Scientific Data Pipelines\nBuild scalable pipelines to ingest, transform, standardize, and connect scientific data.\n\nIntegrate laboratory, OMICS, imaging, assay, phenotype, formulation, consumer, and performance datasets.\n\nAutomate data movement into governed analytical environments.\n\nTranslate approved data-product requirements into maintainable technical designs.\n\n2. Implement Trusted Data Controls\nImplement metadata, provenance, versioning, identity resolution, access, and data-quality controls\n\nMaintain traceability to samples, methods, experimental conditions, transformations, and source systems\n\nImplement approved semantic and ontology requirements\n\nTest transformations, interfaces, and controls for reliable downstream use\n\n3. OperateScalable Data Services\nBuild and maintaininterfaces among repositories, knowledge graphs, analytical tools, computational models, and AI workflows\n\nMonitor data availability, pipeline performance, failures, and dependencies\n\nResolve bottlenecks affecting data access, reuse, and analytical readiness\n\nOwn operational support and reliability for pipelines, interfaces, and data services\n\nJob Qualifications\nBachelors, Mastersor PhD degree in Computer Science, Data Engineering, Software Engineering, or a relatedfield.\n\nStrong hands-on experience building data pipelines, integrations, and scalabledata services.\n\nProficiencyin Python, SQL, or comparable data-engineeringtechnologies.\n\nExperience with structured and unstructured data, databases, APIs, and cloud-based environments.\n\nUnderstanding ofmetadata, provenance, data quality, versioning, access controls, testing, and operational support.\n\nPreferred Qualifications\nFamiliarity with orchestration, data lakes, warehouses, lakehouses, APIs, and cloud platforms\n\nExperience implementing semantic models, ontologies, knowledge graphs, or FAIR data practices\n\nExperience engineering biological, OMICS, imaging, laboratory, healthcare, or complex scientific data\n\nExperience building data foundations for analytics, machine learning, or AI\n\nAbout us\nWe produce globally recognized brands and we grow the best business leaders in the industry. With a portfolio of trusted brands as diverse as ours, it is paramount our leaders are able to lead with courage the vast array of brands, categories and functions. We serve consumers around the world with one of the strongest portfolios of trusted, quality, leadership brands, including Always®, Ariel®, Gillette®, Head & Shoulders®, Herbal Essences®, Oral-B®, Pampers®, Pantene®, Tampax® and more. Our community includes operations in approximately 70 countries worldwide.\nVisit http://www.pg.com to know more.\nOur consumers are diverse and our talents - internally - mirror this diversity to best serve it. That is why we’re committed to building a winning culture based on Inclusion and our ideal candidate is passionate about the same principle: you will join our daily effort of being “in touch” so we craft brands and products to improve the lives of the world’s consumers now and in the future. 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