Data Architect/Modeler

Key Responsibilities Design and implement enterprise-wide data architecture solutions for large-scale financial services environments. Develop conceptual, logical, and physical data models for operational, analytical, and reporting platforms. Architect and support cloud-native data platforms, including modern data lake, data warehouse, and lakehouse architectures. Perform hands‑on data engineering activities, including ETL/ELT pipeline development, data ingestion frameworks, and transformation processes. Design scalable batch and real-time data integration solutions for structured and semi-structured data sources. Support Master Data Management (MDM) initiatives across domains such as client, account, security, and reference data. Collaborate with enterprise architecture, governance, security, compliance, and business teams to define data standards and best practices. Implement data quality, metadata management, data lineage, and data governance frameworks. Optimize data platforms for scalability, performance, reliability, security, and cost efficiency. Support regulatory, audit, risk, and compliance reporting requirements in financial services environments. Participate in cloud migration and modernization initiatives involving legacy and distributed data systems. Enable analytics, reporting, AI/ML, and BI capabilities through trusted and governed enterprise data solutions. Required Skills & Experience 8–10 years of experience in Enterprise Data Architecture and Data Modeling across modern data platforms. Strong hands‑on experience in Data Engineering and development of scalable data pipelines. Proven experience with cloud‑based data platforms and distributed data processing technologies. Strong understanding of data warehouse, data lake, and lakehouse architectures and implementations. Experience with platforms such as Snowflake, Databricks, or similar cloud data platforms. Experience with legacy/on‑prem platforms such as Oracle Exadata or similar data warehouses. Experience designing and implementing ETL/ELT frameworks using modern tools (e.g., Spark-based pipelines, Snowflake Tasks & Streams). Strong experience in data integration and ingestion patterns for large‑scale structured and unstructured data. Experience supporting legacy-to-modern data platform transformation initiatives. Experience with Master Data Management (MDM) and enterprise data governance frameworks. Strong knowledge of metadata management, data lineage, data cataloging, and data quality practices. Experience in financial services data environments with regulatory and compliance requirements. Strong SQL skills with programming/scripting experience in Python, PySpark, or Snowpark. Hands‑on experience with dbt for data transformation, modeling, testing, documentation, and version control integration. Experience with cloud platforms (Azure, AWS, or GCP) and integration with Snowflake and Databricks. Familiarity with API‑based integrations, real‑time/streaming pipelines (e.g., Kafka, Spark Streaming), and event‑driven architectures. Understanding of data security, compliance, and governance practices including role‑based access control, encryption, and data masking. Experience working in Agile environments and cross‑functional delivery teams. Preferred Qualifications Experience supporting enterprise modernization and cloud transformation programs. Exposure to real‑time analytics and large‑scale distributed data platforms. Knowledge of enterprise data governance and architecture frameworks. Strong communication and stakeholder management skills. Experience in Financial Services, Investment, or Wealth Management domains preferred. #J-18808-Ljbffr