Lenovo

Responsibilities:

1. Following digital transformation strategy, plan enterprise-grade digital capabilities in data and analytics domain；
2. Based on composable architecture concept and Lenovo unique requirements, design a Lenovo specific and harmonized data and analytics application architecture；
3. Design end-to-end Data/Dev/ML Ops pipelines based on in-depth understanding of cloud platforms, data governance and management, AI lifecycle, and business capabilities；
4. Design and refine enterprise data architecture to realize different business requirements on data, i.e., EDW, data lake, downstream systems data consumption；
5. Collaborate with DT and IT to define and optimize the process and standards to manage enterprise common data model；
6. Set up review process for data & analytics application architecture. Assess data & analytics solution and platform on their compliance with overall architecture design. Provide guidance, direction and authorization to project team to architect solution & platform with end-to-end pipeline to ensure standard and best practice are followed；
7. Collaborate with data analyst, data engineer, and data science teams to identify common building blocks, lead development to architect and efficiently deliver reusable digital capabilities that fulfill business needs and align with harmonized application architecture；
8. Act as an expert within the company, educate data & analytics community around Data/Dev/ML Ops, architecture design and governance, data management and data governance best practices；
9. Continuously integrate state-of-the-art architecture concept into data and analytics solutions and platforms；
10. Collaborate with domain platform architects across Digital Transformation organization to achieve a synergized architecture design within the company.

Requirements:

1. From an accredited college or university, with bachelor’s degree in computer science, Computer Engineering, Information System or equivalent. Master’s degree from an accredited college or university is preferred;
2. Minimum 5 years’ experience in information management, data management, ML/analytics solutions implementation with end-to-end development lifecycle support;
3. Experienced in the DataOps, MLOps, DevOps practices;
4. Demonstrated understanding of enterprise-level data warehouse, data modelling, big data, BI and analytics;
5. Good understanding of machine learning solution development lifecycle methodology and data engineering process;
6. A comprehensive understanding of data transformation (ETL) process, data factory, data lake, and analytics products;
7. Experience with two or more technologies in any of the following areas including: SQL Server, MS Azure, Teradata, Hadoop, Power BI, Python, Java;
8. Experienced and knowledgeable in composable architecture concept;
9. Fluent Chinese & English communication skills;
10. Excellent organizational and interpersonal skills.