Top 5 Core Skills
Enterprise Data Architecture & Modeling: 8–10 years of experience designing conceptual, logical, and physical data models across operational, analytical, and modern cloud platforms.
Legacy-to-Cloud Migration: Proven experience modernizing legacy on-premise data warehouses (specifically Oracle Exadata) to modern cloud ecosystems (Databricks, Snowflake, Azure/AWS/GCP).
Master Data Management (MDM) & Governance: Deep expertise in implementing enterprise MDM solutions—specifically GoldenSource—and establishing data governance, lineage, cataloging, and quality frameworks.
Hands-On Engineering & dbt: Strong capability to not just design but also build. Requires proficiency in dbt (Data Build Tool) for data transformation and ELT pipelines, alongside strong SQL, Python, PySpark, or Snowpark skills.
Financial Services & Regulatory Expertise: Industry experience in banking or wealth management, with a solid understanding of regulatory, risk, and compliance-driven data requirements (e.g., RBAC, data masking).
Looking for:
-implementing new MDM solution with golden source
-Financial Service Industry experience; Experience working with financial services data domains and regulatory/compliance-driven data environments.
-Data Architect & Modeler across platforms; 8–10 years of experience in Enterprise Data Architecture and Data Modeling across modern data platforms.
-Hands-on engineering
-Databricks (cloud platforms)
-Experience Migrating from Legacy/Oracle to Cloud
-Implement/Support Master Management Data platforms; Experience with Master Data Management (MDM) and enterprise data governance frameworks
-Golden Source Data Management
-Will be supporting different multi-year projects not just one
-On-prem data platforms / legacy data warehouses (e.g. Oracle Exadata)
-Experience with dbt (Data Build Tool) for: Data transformation and modeling, ELT pipeline development within Snowflake/Databricks, Modular, reusable SQL-based data workflows, Data testing, documentation, and version control integration
-Experience with cloud platforms such as Azure, AWS, or GCP, including their integration with Snowflake and Databricks.
Job Description
We are seeking an experienced Data Architect with strong Data Modeling expertise and hands-on Data Engineering capabilities to support enterprise data initiatives within the Financial Services industry. The ideal candidate will have experience designing scalable cloud-based data platforms, developing enterprise data models, and supporting modern data architecture initiatives across large and complex environments.
This role requires expertise in enterprise data architecture, cloud data platforms, Master Data Management (MDM), and modern data engineering practices. The candidate should be comfortable working closely with business stakeholders, data governance teams, application teams, and analytics organizations to deliver scalable, secure, and high-performing data solutions aligned with enterprise standards and regulatory requirements.
Key Responsibilities:
• Design and implement enterprise-wide data architecture solutions for large-scale financial services environments.
• Develop conceptual, logical, and physical data models supporting operational, analytical, and reporting platforms.
• Architect and support cloud-native data platforms including modern data lake and data warehouse ecosystems.
• Perform hands-on data engineering activities including development of ETL/ELT pipelines, data ingestion frameworks, and transformation processes.
• Design scalable batch and real-time data integration solutions for structured and semi-structured data.
• Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains.
• Collaborate with enterprise architecture, governance, security, compliance, and business teams to establish data standards and best practices.
• Implement data quality, metadata management, lineage, and governance frameworks.
• Optimize data platforms for scalability, reliability, performance, and cost efficiency.
• Support regulatory, audit, risk, and compliance reporting requirements within U.S. financial industry environments.
• Participate in cloud migration and modernization initiatives involving legacy and distributed data systems.
• Enable analytics, reporting, AI/ML, and business intelligence 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.
• Hands-on experience with Data Engineering and development of scalable, modern data pipelines.
• Proven experience with cloud-based data platforms and distributed data processing technologies.
• Strong understanding of data warehouses, data lake, and lakehouse architecture, including implementation on Modern data platforms.
• Cloud data platforms (e.g. Snowflake, Databricks, or similar)
• On-prem data platforms / legacy data warehouses (e.g. Oracle Exadata)
• Experience designing and implementing ETL/ELT frameworks using tools native to both platforms (e.g., Spark-based pipelines, Snowflake tasks and streams).
• Experience with data integration and ingestion patterns for large-scale structured and unstructured data across platforms.
• Experience designing modern data platforms for legacy transformation initiatives.
• Experience with Master Data Management (MDM) and enterprise data governance frameworks.
• Knowledge of metadata management, data lineage, data cataloging, and data quality processes.
• Experience working with financial services data domains and regulatory/compliance-driven data environments.
• Strong SQL expertise along with programming/scripting experience in Python, PySpark, or Snowpark.
• Experience with dbt (Data Build Tool) for: Data transformation and modeling, ELT pipeline development within Snowflake/Databricks, Modular, reusable SQL-based data workflows, Data testing, documentation, and version control integration
• Experience with cloud platforms such as Azure, AWS, or GCP, including their integration with Snowflake and Databricks.
• Familiarity with API integration, real-time/streaming data pipelines (e.g., Kafka, Spark Streaming), and event-driven architectures.
• Understanding of security, compliance, and governance standards, including role-based access, data masking, and encryption in both Snowflake and Databricks.
• Experience working in Agile delivery models and collaborating with cross-functional teams.
Preferred Qualifications:
• Experience supporting enterprise modernization and cloud transformation initiatives.
• Exposure to real-time analytics and large-scale distributed data platforms.
• Knowledge of data governance and enterprise architecture frameworks.
• Strong communication and stakeholder management skills.
• Financial Services or Investment/ Wealth Management domain experience preferred.
Education:
Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
Job Responsibilities
• Design and implement enterprise-wide data architecture solutions for large-scale financial services environments.
• Develop conceptual, logical, and physical data models supporting operational, analytical, and reporting platforms.
• Architect and support cloud-native data platforms including modern data lake and data warehouse ecosystems.
• Perform hands-on data engineering activities including development of ETL/ELT pipelines, data ingestion frameworks, and transformation processes.
• Design scalable batch and real-time data integration solutions for structured and semi-structured data.
• Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains.
• Collaborate with enterprise architecture, governance, security, compliance, and business teams to establish data standards and best practices.
• Implement data quality, metadata management, lineage, and governance frameworks.
• Optimize data platforms for scalability, reliability, performance, and cost efficiency.
• Support regulatory, audit, risk, and compliance reporting requirements within U.S. financial industry environments.
• Participate in cloud migration and modernization initiatives involving legacy and distributed data systems.
• Enable analytics, reporting, AI/ML, and business intelligence capabilities through trusted and governed enterprise data solutions.