Description

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 lake, data warehouse, and lakehouse ecosystems.
•    Design scalable enterprise data platforms aligned with architecture, security, governance, and regulatory standards.
•    Perform hands-on data engineering, including ETL/ELT pipeline development, data ingestion, and transformation.
•    Design batch and real-time data integration solutions for structured, semi-structured, and unstructured data.
•    Implement and support Master Data Management platforms and Golden Source data solutions.
•    Support MDM initiatives across security, account, client, and reference data domains.
•    Develop reusable and modular data transformation workflows using dbt.
•    Build and optimize data pipelines using Spark, PySpark, Snowflake, and Databricks technologies.
•    Support legacy modernization and migrations from on-premises platforms such as Oracle Exadata to cloud-based data platforms.
•    Implement data quality, metadata management, data lineage, cataloging, and governance frameworks.
•    Collaborate with enterprise architecture, governance, security, compliance, application, analytics, and business teams.
•    Optimize data platforms for scalability, reliability, performance, and cost efficiency.
•    Support regulatory, audit, risk, and compliance reporting requirements within U.S. financial environments.
•    Enable analytics, business intelligence, AI/ML, and reporting capabilities using trusted and governed enterprise data.
•    Participate in multiple enterprise cloud modernization and data transformation programs.

Required Skills and Experience
•    8–10 years of experience in Enterprise Data Architecture and Data Modeling.
•    Strong experience developing conceptual, logical, and physical data models.
•    Hands-on Data Engineering experience with modern and scalable data pipelines.
•    Strong Financial Services industry experience.
•    Experience working with regulatory and compliance-driven data environments.
•    Strong experience implementing and supporting Master Data Management (MDM) platforms.
•    Experience with Golden Source Data Management and enterprise reference/master data solutions.
•    Strong experience with Databricks and modern cloud-based data platforms.
•    Experience with Snowflake, Databricks, or similar cloud data platforms.
•    Experience with on-premises and legacy data warehouse platforms, including Oracle Exadata.
•    Proven experience migrating legacy or Oracle-based data platforms to cloud environments.
•    Strong understanding of data warehouse, data lake, and lakehouse architectures.
•    Experience designing ETL/ELT frameworks using Spark-based pipelines, Snowflake Tasks, Streams, or equivalent technologies.
•    Strong experience with dbt (Data Build Tool) for data transformation, modeling, and ELT development.
•    Experience developing modular and reusable SQL-based data workflows using dbt.
•    Experience with dbt testing, documentation, and source/version control integration.
•    Strong SQL development and optimization skills.
•    Programming or scripting experience with Python, PySpark, or Snowpark.
•    Experience with data governance, metadata management, data lineage, data cataloging, and data quality.
•    Experience with Azure, AWS, or GCP and integration with Snowflake and Databricks.
•    Experience with large-scale data ingestion and integration patterns.
•    Familiarity with API-based integrations and event-driven data architectures.
•    Experience with Kafka, Spark Streaming, or similar real-time data processing technologies.
•    Understanding of data security and governance controls, including role-based access, data masking, and encryption.
•    Experience working in Agile delivery environments and collaborating with cross-functional teams.
•    Strong communication and stakeholder management skills.

Preferred Qualifications
•    Investment Management or Wealth Management domain experience.
•    Experience supporting enterprise modernization and cloud transformation initiatives.
•    Experience with real-time analytics and distributed data platforms.
•    Knowledge of enterprise architecture and data governance frameworks.
•    Experience supporting multiple large-scale, multi-year data transformation programs.

Education
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field

Key Skills

Education

Bachelor's degree