Microsoft Principal Consultant - Data & AI 

Taiwan, Taoyuan City 

468623540

• 20+ years of Data Engineering experience and Strong Experience in driving Enterprise Data Architecture, Azure Data services, Machine learning Offerings, Platform Modernizations.

• Over 10 years of experience in designing and leading Data Warehouse, Lakehouse and analytical solutions using platforms such as Microsoft Fabric, Azure Synapse Analytics, Snowflake, and Teradata

• Strong Experience in large database solutions, on premises, cloud, and hybrid implementations

• Strong experience in Serverless Architecture/Microservices

• Strong experience of full application life cycle design tools and methodologies

• Strong experience in one or more technologies under each of the following

• Big Data stack: Spark, Spark Streaming, Databricks, Kafka, Hadoop, Hive, HDInsight.

• Database Stack: Strong database experience with OLTP/OLAP, data storage mechanism and architecture,

• Columnar (RedShift, Vertica)

• OSS (MySQL,PostgreSQLand MariaDB)

• (MongoDB, Redis, Cassandra - key-value stores, graph databases, RDF triple stores)

• Level 400 knowledge in one (and preferably more) of the database engines, engine stack including engine level debugging for ex; CXPACKET analysis, Disk throttling, storage, IO contentions, Network contentions, performance optimizations.

• Strong experience in Data Engineering:

• Data Architecture: Dimensional Modelling, Lambda/Kappa architecture, Time series data

• Azure Stream Analytics, Azure AnalysisServiceMusthave experience in two or more relational DBMS (Microsoft SQL, Azure Synapse and/or Oracle, Teradata, Netezza)

• Ability to design and drive large Data Migrations using necessary ETL technologies: ADF, SSIS, Talend, Pentaho, Informatica. Drive multi-tenant database designs, security hardening of the data platform.

• Expertisein implementation of Data governance practices using either open source or proprietary tools.

• Expertisein Data Ops

• Good experiencein Agile Methodology &Expertisein Azure DevOps and Setting examples for good engineering practices and coding along the way through automation where possible.

• Industry experience in one or more of the following industries: automotive, energy, travel and transportation, financial services, government, health, manufacturing, media & communications, or retail/supply chain.

• Strong experience in AI/ML:

• Expertisein building and operationalizing ML pipelines, including feature engineering, model training, evaluation, and deployment (MLOps).

• Expertisein Azure ML and Open AI.

• Hands-on experience in Natural Language Processing, Document Intelligence & Indexing or Customer Vision or RAG Framework or AI Search

• Good understanding of Prompt Engineering Basics.

• Experience integrating AI models into enterprise data platforms and driving intelligent automation across business processes.

• Familiarity with Responsible AI principles, including fairness, interpretability, and governance of AI models.

ice to have Skills

• Familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalogue, Data Mesh and Data Modelling.

• Multi-cloud experience a plus - Azure, AWS, Google

AI-First Delivery Leadership

• Embed AI-first principles into delivery workflows,leveragingautomation and intelligent orchestration where applicable.

• Lead end-to-end delivery of complex projects, ensuring solutions are scalable, robust, and aligned with client business outcomes.

• Drive engineering excellence through reusable components, accelerators, and scalablearchitecture.

• Oversee technical execution across multiple projects, ensuring adherence to best practices, quality standards, and compliance requirements.

• Collaborate with clients and internal stakeholders to define strategies, delivery plans, milestones, and risk mitigation approaches.

• Act as a technical point of contact for clients, translating business requirements into scalable technical solutions.

• Ensure delivery models areoptimizedfor modern, AI-native execution, including integration of automation and intelligent processes.

Engineering Excellence

• Champion high-quality engineering practices across all delivery engagements.

• Ensure adherence to coding standards, architectural integrity, and performance benchmarks.

• Promote secure coding, test-driven development, and observability as default practices.

• Encourage continuous learning and technical certifications tomaintaincutting-edgeexpertise.

• Drive adoption of modern delivery methodologies (Agile, DevOps, CI/CD) to ensure robust and scalable solutions.

• Foster a culture of technical rigor, innovation, and accountability within the team.

Innovation & Thought Leadership

• Monitor and evaluate emerging technologies to inform strategic direction.

• Lead innovation in delivery models, reusable assets, and accelerators to enhance efficiency and impact.

• Champion modern thinking and best practices across teams and engagements to foster a culture of continuous improvement.

Client Engagement & Solutioning

• provideexpert guidance throughout the project lifecycle.

• Support pre-sales andsolutioningefforts with estimations, proof-of-concepts, and technical proposals.

• Build andmaintainstrong client relationships, ensuringhigh levelsof satisfaction and value delivery.

Team Management & Mentorship

• Lead and mentor cross-functional teams, fostering a culture of learning, collaboration, and technical excellence.

• Conduct reviews, provide feedback, and support professional development of team members.

Quality & Compliance

• Ensure all deliverables meet quality standards, security, and regulatory requirements.

• Promote secure coding, test-driven development, and observability as default practices.

Strategic Partnering

• Serve as a strategic partner for internal and external stakeholders on key initiatives.

• Provide strategic guidance and execution oversight to ensure alignment with organizational goals.

• Embody our