{"id":744669,"url":"https://alion.io/job/agilent-technologies-global-operations-data-engineer","title":"Global Operations Data Engineer","company":{"id":6171,"name":"Agilent Technologies","domain":"agilent.com","url":"https://alion.io/company/agilent-technologies","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":42,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-28T05:59:10Z"}},"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":47000,"max_usd":114000,"period":"year","method":"role_country_seniority_unknown","sample_n":108},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Digital Twin","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"Rest API","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-11T13:46:57Z","employer_posted_date":"2026-09-11","last_verified_at":"2026-10-28T16:37:34Z","board_verified":true,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":47},"description":"Job Description\nBuild the trusted data foundation that powers intelligent operations.\nJoin Agilent's Global OT & AI Engineering organization and help shape the future of digital manufacturing through modern data engineering.\nAs a Global Operations Data Engineer, you will design, develop, and maintain enterprise data platforms that transform operational data into trusted, reusable, and AI-ready assets. You will build scalable data pipelines, cloud-native data solutions, and enterprise integration services while implementing data governance practices that ensure data quality, consistency, lineage, and reliability across Agilent's global operations.\nWorking closely with Data Architects, AI & Data Scientists, Software Engineers, Manufacturing Engineers, and business stakeholders, you will enable a connected data ecosystem that supports enterprise applications, operational reporting, Artificial Intelligence, Smart Factory initiatives, and executive decision-making.\nWe welcome candidates across multiple career stages. Responsibilities and technical ownership will be aligned with your experience, expertise, and demonstrated capabilities.\nAbout Global OT & AI Engineering\nGlobal OT & AI Engineering builds the digital foundation that powers Agilent's global manufacturing operations.\nOur organization develops enterprise software, AI platforms, cloud-native applications, smart factory technologies, and enterprise data platforms that connect manufacturing sites into one intelligent ecosystem.\nOur mission is to transform operational data into trusted insights and intelligent decisions that improve how products are designed, manufactured, tested, and delivered worldwide.\nOur portfolio includes:\nManufacturing AI & Advanced Analytics\n\nGlobal Operations Control Tower\n\nSmart Factory & Industry 4.0\n\nEnterprise Data Platform\n\nDigital Twin Solutions\n\nManufacturing Knowledge Platform\n\nAI-powered Decision Support\n\nEnterprise Data Exchange Platform\n\nAbout the Role\nModern manufacturing depends on reliable, connected, and trusted data.\nEvery day, enterprise systems, manufacturing equipment, industrial IoT devices, quality systems, and engineering applications generate vast amounts of operational data. The value of this data depends on the ability to integrate, manage, govern, and deliver it efficiently to the people and systems that rely on it.\nAs a Global Operations Data Engineer, you will build the enterprise data platform that powers Agilent's digital operations. You will develop scalable data pipelines, implement data governance capabilities, and create reusable data products that enable analytics, Artificial Intelligence, digital applications, and operational excellence.\nYour work will directly support initiatives such as the Global Operations Control Tower, AI-powered decision support, Smart Factory programs, Digital Twins, and enterprise reporting.\nKey Responsibilities\nEnterprise Data Platform Development\nDesign, develop, and maintain scalable enterprise data pipelines that integrate data from Manufacturing, Supply Chain, Procurement, Logistics, Quality, Engineering, ERP, MES, and Industrial IoT platforms.\n\nBuild reusable data products and curated datasets that support enterprise reporting, analytics, AI applications, and digital products.\n\nDevelop cloud-native data solutions that enable secure, scalable, and high-performance access to operational data.\n\nOptimize data processing, storage, and orchestration to ensure platform reliability and scalability.\n\nData Integration & Engineering\nDevelop and maintain ETL/ELT pipelines supporting enterprise and operational systems.\n\nDesign APIs and integration services that enable seamless data exchange across enterprise applications.\n\nImplement batch, streaming, and event-driven data processing architectures.\n\nCollaborate with Software Engineers to expose trusted data through APIs and enterprise services.\n\nSupport enterprise integration across SAP, MES, PLM, Quality Systems, cloud platforms, and operational technologies.\n\nData Governance & Quality\nImplement enterprise data governance standards and best practices defined by the Data Architecture team.\n\nDevelop automated data quality validation, monitoring, reconciliation, and exception handling processes.\n\nMaintain enterprise metadata, data lineage, and catalog information to improve discoverability and traceability.\n\nSupport Master Data Management (MDM) initiatives by ensuring consistency of critical business data.\n\nMonitor enterprise data quality metrics and proactively resolve data integrity issues.\n\nCollaborate with business stakeholders to identify Critical Data Elements (CDEs) and improve data reliability across operational systems.\n\nPromote governance practices that ensure enterprise data remains accurate, secure, trusted, and fit for business use.\n\nAI & Analytics Enablement\nPrepare and maintain AI-ready datasets supporting Machine Learning and advanced analytics.\n\nCollaborate with AI & Data Scientists to develop feature engineering pipelines and reusable analytical datasets.\n\nSupport semantic data models that enable self-service reporting and enterprise analytics.\n\nImprove accessibility, consistency, and reuse of enterprise data across digital products and AI platforms.\n\nPlatform Reliability & Continuous Improvement\nMonitor the health, availability, and performance of enterprise data pipelines and services.\n\nImplement observability, logging, monitoring, and alerting capabilities for critical data platforms.\n\nApply DevOps and CI/CD best practices to automate deployment, testing, and operational support.\n\nContinuously evaluate emerging technologies and engineering practices to improve the enterprise data platform.\n\nCross-functional Collaboration\nPartner with Data Architects to implement enterprise data architecture, standards, and integration strategies.\n\nCollaborate with Manufacturing, Supply Chain, Quality, Engineering, and IT teams to understand business requirements and deliver scalable data solutions.\n\nWork closely with Software Engineers and AI teams to integrate trusted data into enterprise applications and intelligent solutions.\n\nSupport enterprise digital transformation initiatives across Agilent's global operations.\n\nTechnology Environment\nYou will work with a modern enterprise technology stack, including:\nProgramming & Data Engineering\nPython\n\nSQL\n\nApache Spark\n\ndbt\n\nCloud & Data Platforms\nMicrosoft Azure\n\nAWS\n\nMicrosoft Fabric\n\nAzure Data Lake\n\nSnowflake\n\nIntegration & Streaming\nREST APIs\n\nAzure Event Hub\n\nApache Kafka\n\nData Factory\n\nEnterprise Systems\nSAP ERP / S/4HANA\n\nSAP ME / MES\n\nPLM\n\nQuality Management Systems\n\nIndustrial IoT Platforms\n\nData Governance\nMicrosoft Purview\n\nMaster Data Management (MDM)\n\nData Catalog\n\nMetadata Management\n\nData Lineage\n\nData Quality Monitoring\n\nDevOps\nGitHub\n\nAzure DevOps\n\nDocker\n\nAnalytics\nPower BI\n\nMicrosoft Fabric\n\nStreamlit (preferred)\n\nQualifications\nRequired Qualifications\nWe welcome candidates across multiple career stages. Responsibilities and technical leadership will be aligned with your experience and demonstrated capability.\nYou should have:\nBachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Engineering, or a related discipline.\n\nExperience in Data Engineering, Software Engineering, Data Integration, or Enterprise Data Platforms.\n\nStrong programming skills in Python and SQL.\n\nExperience building and maintaining scalable data pipelines and cloud-based data solutions.\n\nUnderstanding of data modeling, ETL/ELT processes, API integration, and enterprise data management principles.\n\nStrong analytical, problem-solving, and communication skills.\n\nPassion for building modern data platforms that enable digital transformation.\n\nPreferred Qualifications\nExperience in one or more of the following areas is advantageous:\nManufacturing, Supply Chain, Quality, or Enterprise Operations\n\nCloud-native Data Platforms (Azure, AWS, Databricks, Microsoft Fabric)\n\nData Lakehouse or Data Warehouse Architecture\n\nEvent-driven Data Processing\n\nData Governance, Metadata Management, or Master Data Management (MDM)\n\nArtificial Intelligence or Advanced Analytics Platforms\n\nIndustry 4.0, Industrial IoT, or Smart Manufacturing\n\nDevOps and CI/CD practices\n\nAdditional Details\nThis job has a full time weekly schedule.Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locationsAgilent Technologies Inc. is an equal opportunity employer. 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