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 Google Cloud Platform, including their integration with Snowflake and Databricks.
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.
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.
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 Google Cloud Platform, 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.
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