Agilent Technologies
Agilent Technologies
Agilent Technologies develops analytical and laboratory technologies used across biopharmaceutical research, food and beverage testing, clinical diagnostics, and environmental analysis. Its portfolio includes gas and liquid chromatography, mass spectrometry, laboratory supplies, software, and informatics, supported by services for laboratory management, maintenance, and instrument repair. The company also applies these technologies to genomics, pathology, and cancer research, with an emphasis on advancing science through sustainable solutions.

Analytics Engineering Lead - Agilent Technologies - Santa Clara Onsite

Lead the development of governed lakehouse data products for Agilent Technologies’ global operations. Enable executive reporting, AI initiatives, and decision intelligence across supply chain and manufacturing.

Description

  • Collaborate with IT, enterprise architecture, and platform enablement teams to expand the Global Operations data lakehouse and reporting environment.
  • Lead analytical data architecture for Global Operations, spanning silver- and gold-layer products, dimensional models, semantic assets, and integration patterns.
  • Create cleansed, conformed, consumption-ready data products from enterprise-system data.
  • Build and oversee certified semantic models, common KPI definitions, and reusable business logic.
  • Apply governance controls for data quality, reconciliation, metadata, lineage, certification, and ownership.
  • Automate and improve transformation workflows with SQL, Python, PySpark, and cloud-native engineering services.
  • Establish DataOps and platform management practices covering version control, automated testing, CI/CD, monitoring, performance, security, and production support.
  • Develop AI-ready data products and knowledge assets for advanced analytics, machine learning, generative AI, and decision intelligence.

Requirements

  • Bachelor’s degree in data engineering, data science, computer science, statistics, or a related discipline, or equivalent professional experience.
  • At least eight years of experience in analytics engineering, data modeling, data warehousing, or data platform development.
  • Advanced knowledge of dimensional modeling and modern data architecture.
  • Expert SQL skills with strong Python and PySpark capability.
  • Background designing scalable pipelines, lakehouse architectures, and governed data products.
  • Experience with Microsoft Fabric, Databricks, Snowflake, or comparable platforms, including orchestration and transformation frameworks.
  • Experience architecting enterprise data products, governance frameworks, and reusable analytical assets such as KPI frameworks, metadata, and data quality controls.
  • Strong understanding of ERP and operational processes, including SAP ECC structures and the MM, PP, QM, and SD modules.
  • Ability to align technical and business stakeholders and explain architecture within a matrixed organization.
  • Experience using GitHub, Confluence, and Jira.
  • Master’s degree preferred.
  • Familiarity with Oracle Agile PLM preferred.
  • Experience supporting global supply chain, manufacturing, quality, or regulatory operations preferred.
  • Experience contributing to ERP modernization programs preferred.
  • Experience increasing adoption of enterprise data products, governance practices, and self-service analytics preferred.
  • Relevant Microsoft Fabric, cloud, or data engineering certifications, including MS DP-600 or MS DP-700, preferred.

Benefits

  • Eligibility for a bonus.
  • Eligibility for stock awards.
  • Employee benefits package.
  • Travel requirement of 10%.

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