AvePoint - Data Architect (Managed Services)

• Data Acquisition, Cleansing & Integration • Design and implement enterprise-wide data collection strategies across heterogeneous source systems (ETRM, ERP, CRM, external APIs, flat files, and databases). • Develop robust data cleansing and standardization pipelines to ensure data accuracy, consistency, and completeness across all data sources. • Build data integration frameworks to consolidate data from disparate systems into a unified enterprise data warehouse, handling schema evolution and data drift. • Implement Change Data Capture (CDC) mechanisms and incremental data loading strategies to ensure near real-time data freshness. • Data Warehouse Architecture & Modeling • Lead the design and construction of the enterprise data warehouse (EDW) using industry-proven methodologies (Inmon, Kimball, or Data Vault). • architect data marts for specific business domains (finance, sales, supply chain, operations) with clear separation of concerns. • Select and implement appropriate data warehouse technologies based on workload characteristics: batch analytics (Hive, Spark SQL), real-time analytics (ClickHouse, Apache Doris, StarRocks), or MPP databases (Greenplum, Vertica). • Design layered data architectures (ODS → DWD → DWS → ADS / Bronze → Silver → Gold) with clear data flow and transformation logic at each layer. • ETL/ELT Pipeline Development • Design, build, and maintain scalable ETL/ELT pipelines using Apache Spark, Flink, Hive, MapReduce, or proprietary ETL tools (Informatica, Talend, Kettle). • Implement workflow orchestration using Apache Airflow, DolphinScheduler, Azkaban, or Oozie to schedule, monitor, and manage data pipelines. • Establish data quality frameworks: automated validation rules, anomaly detection, data profiling, and quality scorecards to ensure pipeline reliability and data trustworthiness. • Build comprehensive monitoring and alerting systems for pipeline health, data freshness, and SLA compliance. • Optimize pipeline performance through partitioning, bucketing, indexing, and query tuning strategies. • Business Intelligence & Reporting • Develop enterprise BI reporting systems and interactive dashboards using tools such as Apache Superset, FineBI, Tableau, Power BI, or Quick BI. • Design executive dashboards, operational reports, and self-service analytics interfaces tailored to business stakeholders across finance, trading, operations, and management. • Implement metrics frameworks (KPIs, OKRs) and ensure data definitions are standardized, documented, and consistently applied across all reports. • Work directly with business units to understand analytical requirements and translate them into effective data models and visualizations. • Data Governance & Standards • Design and implement enterprise data governance frameworks from the ground up, including data ownership policies, stewardship roles, and governance workflows. • Build a centralized data catalog (using Apache Atlas, DataHub, Amundsen, or similar) documenting all data assets, business glossaries, and metadata. • Implement data lineage analysis systems to track data flow from source to consumption, enabling impact analysis and root cause diagnosis. • Establish data security and audit frameworks: access control (RBAC/ABAC), data masking, encryption policies, sensitive data discovery, and compliance audit trails. • Define and enforce data standards: naming conventions, data dictionaries, master data management (MDM) rules, and data quality SLAs. • Ensure regulatory compliance with data protection laws (GDPR, PIPL, industry-specific regulations) through governance controls and documentation.