Amazon Web Services (AWS) is looking for a Sr. Analytics Specialist Solutions Architect to join our team in Taiwan and help customers unlock the full value of their data. In this role, you will serve as a Subject Matter Expert (SME), working directly with enterprise customers to design cloud-based analytics solutions — spanning data warehousing, data lake architectures, real-time analytics, and modern data platform (資料中台) strategies. We are looking for someone with a data scientist's mindset — someone who deeply understands how data flows from ingestion to insight, and who can translate complex analytical requirements into scalable, production-grade architectures on AWS. You will help customers build the data foundation required to power advanced analytics, machine learning, and AI initiatives. This is a deeply technical, customer-facing role for someone who thrives on solving complex data challenges and enabling organizations to become truly data-driven.
Key job responsibilities
- Analytics Architecture Design: Provide deep technical expertise for customer engagements involving AWS analytics services, including Amazon Redshift, Amazon Athena, Amazon EMR, AWS Glue, Amazon Kinesis, Amazon OpenSearch, Amazon QuickSight, and the broader data ecosystem.
- Data Platform Strategy (資料中台): Help customers design and implement modern data platform architectures — including centralized data lakes, data mesh patterns, data products, metadata management, and data governance frameworks that enable self-service analytics across the organization.
- Data Science Enablement: Bridge the gap between data engineering and data science teams, designing architectures that enable efficient feature engineering, experimentation, model training data pipelines, and analytical workloads at scale.
- Solution Design & Deployment: Design and deploy scalable, high-performance data warehousing, data lake, and real-time analytics solutions tailored to customer business requirements.
- Field Enablement: Create enablement materials for the broader SA population to help them understand how to integrate AWS analytics solutions into customer architectures.
- Cross-functional Collaboration: Partner with account teams, specialist sellers, Professional Services, and partners to drive analytics adoption and deliver comprehensive data solutions.
Basic Qualifications
- Bachelor's degree or above in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
- 8+ years of experience in specific technology domain areas (e.g., data engineering, analytics, data science, cloud computing, systems engineering, software development).
- 3+ years of design, implementation, or consulting experience in data analytics applications and infrastructures.
- Hands-on experience with at least two of the following:
- Data warehousing platforms (Amazon Redshift, Teradata, Oracle DW, Snowflake, BigQuery)
- Data lake architectures (S3-based data lakes, Apache Iceberg, Delta Lake, Hudi)
- Big data processing frameworks (Apache Spark, Hadoop, Flink, Kafka)
- ETL/ELT pipelines and data integration (AWS Glue, dbt, Airflow, Informatica)
- Data science fluency: Understanding of statistical methods, machine learning concepts, and how data platforms serve advanced analytics and ML workloads.
- Strong communication skills in both Mandarin Chinese and English (written and verbal).
Preferred Qualifications
- Data platform / 資料中台 expertise: Proven experience designing enterprise-scale data platforms that enable self-service analytics, data products, and cross-functional data sharing (data mesh, data marketplace, domain-oriented data ownership patterns).
- Data science background: Prior experience as a data scientist or ML engineer, with hands-on expertise in Python (pandas, PySpark, scikit-learn), R, or similar — able to understand and advise on feature engineering, experimentation design, and analytical workflows.
- Real-time analytics: Experience designing streaming analytics architectures using Kinesis, Kafka, Flink, or similar for real-time dashboards, alerting, and event-driven data pipelines.
- Data governance & quality: Experience implementing data cataloging (AWS Glue Data Catalog, DataZone), data quality frameworks, lineage tracking, and access control policies at enterprise scale.
- Visualization & BI: Experience with business intelligence tools (Amazon QuickSight, Tableau, Power BI, Looker) and building self-service analytics layers for business users.
- Migration expertise: Hands-on experience migrating legacy data warehouses (Teradata, Netezza, Oracle) or Hadoop clusters to AWS analytics services.
- AWS experience: Deep experience with AWS analytics stack including Redshift (including Serverless, data sharing, Redshift ML, zero-ETL), Athena, Glue, Lake Formation, Kinesis, EMR, OpenSearch, QuickSight, and SageMaker Lakehouse.
- GenAI integration: Understanding of how analytics platforms integrate with GenAI (e.g., natural language querying, AI-powered data insights, vector embeddings for semantic analytics).
- Advanced degree: Master's or PhD in Data Science, Statistics, Computer Science, or equivalent quantitative field.
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