Senior Data Architect

该职位来源于猎聘 Job Overview

As a core technical backbone of the Data & AI Team, you will be responsible for designing, building, and optimizing enterprise-level big data architectures, leading the planning and implementation of data platforms. Combining SoftwareOne's business scenarios, you will integrate Databricks, Snowflake, and Microsoft data platform technologies to promote data assetization and intelligence, providing solid data architecture support for business decisions. You will collaborate closely with cross functional teams and be proficient in conducting work communication in English to ensure the feasibility, security, and scalability of data architecture solutions.

Key Responsibilities

• Lead the design and implementation of enterprise-level big data architectures, including the entire process of data collection, storage, computing, governance, and distribution. Develop reasonable architecture solutions based on business needs, balancing performance, cost, and security.
• Be responsible for the technical selection and optimization of data platforms, deeply apply Databricks, Snowflake, and Microsoft data platforms (such as Azure

Data Factory, Azure Synapse Analytics, etc.), and lead the deployment, debugging, and operation and maintenance optimization of related platforms.

• Promote the construction of data governance systems, formulate data standards and data quality specifications, solve complex technical problems in data architectures, and improve data availability and accuracy.
• Collaborate closely with business departments, development teams, and AI teams to understand business needs, convert business needs into data architecture solutions, and support data-driven business decisions and product innovation.
• Participate in the team's technical planning and technical accumulation, guide junior data engineers in their work, and promote the improvement of the team's technical capabilities; participate in industry technical exchanges and introduce cutting-edge data architecture concepts and practices.
• Conduct work communication in English throughout the process, including cross departmental collaboration, project reporting, technical docking, etc., to ensure efficient and smooth communication.

Requirements

Core Technical Requirements

• Proficient in the design and implementation of big data architectures, familiar with the big data ecosystem (Hadoop, Spark, Flink, etc.), and have rich experience in building and operating enterprise-level big data platforms.
• Have solid experience in using Databricks and Snowflake, be able to proficiently use the two platforms for data processing, computing optimization, data modeling and other operations, and priority will be given to those with relevant project implementation experience.
• Familiar with Microsoft data platform-related technologies, including but not limited to Azure Data Factory, Azure Synapse Analytics, SQL Server, etc., and be able to integrate the Microsoft ecosystem with big data architectures to complete solution design.
• Master knowledge related to data modeling, data governance, and data security, and have practical experience in data quality control and data asset sorting.
• Familiar with at least one programming language (such as Python, Scala, Java), have certain code development and debugging capabilities, and be able to write data processing scripts or tools.

Competency And Experience Requirements

• Bachelor's degree or above in Computer Science, Big Data, Statistics or related majors, with 5+ years of work experience in data architecture, big data development, etc. Priority will be given to those with work experience in foreign-funded or multinational enterprises.
• Excellent English communication skills (fluent in listening, speaking, reading and writing), able to proficiently use English for work reporting, program communication, email correspondence and cross-departmental collaboration, and can adapt to an all English work environment.
• Strong problem analysis and solving capabilities, able to independently respond to complex technical challenges in data architectures, and have good logical thinking and pressure resistance.
• Good team spirit and communication skills, able to quickly understand business needs and promote the implementation of cross-departmental projects; priority will be given to those with team management or technical guidance experience.
• Understanding of cloud computing and AI-related technologies (such as machine learning, deep learning), and additional points for those with experience in combining data architecture with AI scenarios.