Analytics Engineering Lead

techstreet; Penang; Sur place; MYR 180 000 - 320 000; Plein temps

Résumé du poste

techstreet in Penang is driving a Global Operations analytics program to scale the data lakehouse and reporting ecosystem. You will own the analytical data architecture, including silver and gold layer data products, dimensional models, and semantic assets, aligned with enterprise standards to enable executive reporting and decision intelligence.

The role designs governed semantic models, KPI frameworks, and reusable logic, establishing data quality controls, metadata lineage, and DevOps

Qualifications

• Bachelor's degree in data engineering, data science, computer science, statistics, or related field; equivalent experience.
• 8+ years of analytics engineering, data warehousing, or data platform development with deep dimensional modeling expertise.
• Expert-level SQL and strong Python/PySpark for scalable data pipelines and lakehouse architectures.
• Experience with modern cloud data platforms such as Microsoft Fabric, Databricks, Snowflake, including orchestration & transformation frameworks.
• Experience architecting enterprise data products, governance frameworks, KPI metadata, and data quality controls for scalable analytics.
• Understanding of ERP processes (SAP ECC MM/PP/QM/SD); Oracle Agile PLM familiarity preferred.
• Ability to influence technical and business stakeholders and communicate architectural concepts.

Responsabilités

• Own the Global Operations analytical data architecture, including silver and gold-layer data products and dimensional models.
• Design and build cleansed, conformed, consumption-ready data products integrating data across enterprise systems.
• Develop semantic models, standardized KPI frameworks, and reusable analytics assets for trusted performance measurement.
• Implement data governance practices including data quality controls, metadata, lineage, certification, and ownership standards.
• Automate and optimize data transformations using SQL, Python, PySpark and cloud-native data engineering services.
• Establish DataOps and platform management practices including CI/CD, monitoring, security, and production support.
• Design AI-ready data products and knowledge assets for advanced analytics and decision intelligence.

Connaissances

Analytical thinking

Stakeholder communication

Data modeling

Governance understanding

Formation

Bachelor's degree in Data Engineering / CS / Statistics or equivalent

Master's degree preferred

Outils

SQL

Python

PySpark

Databricks

Snowflake

Microsoft Fabric

Confluence

Jira

GitHub

Description du poste

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.

Qualifications

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.

Preferred Qualifications

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.