Description

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

Key Skills

Education

Bachelor's degree