Data Architect (Managed Services)

About the role

A seasoned Data Architect is sought for a managed services environment, responsible for the end-to-end design and implementation of the data architecture, from data ingestion and cleansing through dimensional modeling, warehouse construction and BI dashboard delivery. The role also establishes data governance frameworks including data catalogs, lineage tracking and security audit systems. Experience in finance, international trade or manufacturing is highly valued.

Key responsibilities

• Design data acquisition, cleansing and integration strategies across ETRM, ERP, CRM, APIs and flat files, and implement CDC and incremental loading
• Build the enterprise data warehouse using Inmon, Kimball or Data Vault methods, and design star, snowflake and constellation schemas
• Select and implement data warehouse technologies (Spark, ClickHouse, Doris, StarRocks, Greenplum, Vertica) and layered architectures (ODS/DWD/DWS/ADS)
• Develop ETL/ELT pipelines with Airflow, DolphinScheduler or similar, and set up data quality and monitoring systems
• Build BI dashboards and reporting systems with tools such as Superset, Tableau, Power BI or Quick BI, and define KPIs and OKRs
• Establish data governance: data catalog, lineage, RBAC/ABAC access control, data masking, encryption and compliance with GDPR and PIPL

Required profile

• Bachelor's degree or above in a technical field; 3+ years in data development, engineering or architecture
• Proven track record of building production data warehouse systems
• Deep experience in financial services, international trade or manufacturing
• Preferred: MDM, data mesh/fabric, data versioning, energy trading or market risk knowledge, and CDMP or DAMA-DMBOK certification

Required skills

• Expert SQL and Python; dimensional modeling (Kimball, Inmon, Data Vault 2.0)
• ETL/ELT tools: Spark, Flink, Hive, Informatica, Talend, Kettle
• Orchestration: Airflow, DolphinScheduler, Azkaban, Oozie
• Data warehouse databases: ClickHouse, Doris, StarRocks, Hive, Greenplum, Vertica, Snowflake
• BI tools: Superset, FineBI, Tableau, Power BI, Quick BI
• Data governance: data catalog, lineage, data quality monitoring, security audits
• Relational databases (PostgreSQL, MySQL, Oracle, SQL Server) and big data stack (HDFS, YARN, Kafka, Hadoop)