Analytics Engineering Lead

Agilent Technologies•Singapore, CA

2d•$143,760 - $269,550•Onsite

About The Position

The Analytics Engineering Lead, within the Operations Intelligence & Analytics organization, orchestrates the transformation of complex, raw data into trusted, business-ready data assets that power executive dashboards, performance metrics and advanced analytics across Global Operations. Working at the intersection of data engineering and business analytics, this role applies modern software engineering and data modeling practices to develop enterprise-grade data products that support decision-making across Global Operations while advancing the maturity of the organization's data lakehouse ecosystem. As a key partner to leaders across Supply Chain, Manufacturing, Quality, Regulatory Affairs, and other Global Operations functions, the Analytics Engineering Lead combines deep technical expertise with strong business acumen to translate strategic priorities into reusable, high-value data solutions. Through close collaboration with cross-functional stakeholders, this role enables scalable analytics, strengthens data foundations, and accelerates the organization's digital, AI, and data science capabilities.

Requirements

Bachelor's degree in Data Engineering, Data Science, Computer Science, Statistics, or a related field, or equivalent professional experience; Master's degree preferred.
8+ years of experience in analytics engineering, data modeling, data warehousing, or data platform development, with deep expertise in dimensional modeling and modern data architectures.
Expert-level SQL and strong proficiency in Python/PySpark, with experience building scalable data pipelines, lakehouse architectures, and governed data products.
Highly experienced with modern cloud data platforms such as Microsoft Fabric, Databricks, Snowflake, or equivalent technologies, including orchestration and transformation frameworks.
Demonstrated experience architecting enterprise data products, governance frameworks, and reusable analytical assets, including KPI frameworks, metadata, and data quality controls, to support scalable analytics, machine learning, AI solutions.
Strong understanding of ERP and operational business processes, including SAP ECC data structures and modules such as MM, PP, QM, and SD; familiarity with Oracle Agile PLM is preferred.
Ability to influence technical and business stakeholders and communicate architectural concepts across a matrixed organization.
Experience leveraging DevOps and collaboration platforms, including GitHub, Confluence, and Jira, to support agile delivery, version control, documentation, and product lifecycle management.

Nice To Haves

Previous experience supporting global supply chain, manufacturing, quality, or regulatory operations in a complex enterprise environment.
Experience supporting ERP modernization initiatives, including legacy ERP environments, system consolidations, data migrations, and transformation of enterprise data assets to modern analytics platforms.
Experience driving adoption of enterprise data products, governance practices, and self-service analytics capabilities.
Relevant Microsoft Fabric, cloud or data engineering certifications such as MS DP-600 or MS DP-700.

Responsibilities

Partner with IT, enterprise architecture, and platform enablement teams to scale the Global Operations enterprise data lakehouse and reporting ecosystem.
Own the Global Operations analytical data architecture, including silver and gold-layer data products, dimensional models, semantic assets, and integration patterns aligned with enterprise standards.
Design and build cleansed, conformed, and consumption-ready data products that integrate data across enterprise systems and support executive reporting, analytics, and decision intelligence.
Develop and govern certified semantic models, standardized KPI frameworks, and reusable business logic that establish a single source of truth for performance measurement across Global Operations.
Implement data governance practices including data quality controls, reconciliation, metadata, lineage, certification, and ownership standards to ensure trusted reporting assets.
Automate and optimize data transformation workflows using SQL, Python, PySpark, and cloud-native data engineering services.
Establish DataOps and platform management practices including source control, automated testing, CI/CD, monitoring, performance optimization, security controls, and production support.
Design AI-ready data products and knowledge assets that support advanced analytics, machine learning, generative AI, and decision intelligence capabilities.