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.

Posted By