We are seeking a highly experienced Senior Data Engineer to support the design development and optimization of large-scale data platforms within a financial services environment. This role focuses on building robust data pipelines enabling advanced credit risk analytics and ensuring compliance with regulatory standards.
The ideal candidate will bring deep expertise in credit and counterparty risk modern data engineering technologies and cloud-based data platforms while collaborating closely with cross-functional teams including risk quant and compliance stakeholders.
Key Responsibilities
Data Architecture & Engineering
Lead architecture and technical design discussions for credit risk data platforms
Design and implement scalable batch and streaming data pipelines using PySpark
Build ingestion frameworks for structured and unstructured data from upstream systems into cloud storage (e.g. S3/ADLS)
Implement data processing workflows using Medallion Architecture (Bronze Silver Gold layers)
Data Processing & Optimization
Develop and optimize transformation logic for large-scale financial datasets
Ensure high data quality auditability and regulatory compliance
Apply optimization techniques such as partitioning indexing and schema evolution
Model and optimize risk metrics (e.g. PD LGD EAD EPE PFE CVA) for analytics and reporting
Platform Integration & Operations
Integrate with external risk engines and support orchestration of complex batch processes
Ensure platform reliability observability and data lineage tracking
Implement and maintain security standards (IAM encryption authentication protocols)
Troubleshoot production issues and provide ongoing support
Collaboration & Documentation
Collaborate with data scientists risk analysts and business stakeholders
Contribute to API design and data contracts for internal and external consumers
Maintain comprehensive technical documentation for audit and compliance purposes
Participate in Agile development processes and ceremonies
Required Qualifications
12 years of experience in data engineering or data development
Strong experience within financial services particularly credit or counterparty risk
Expertise in regulatory frameworks such as Basel III/IV IFRS 9 CECL or FRTB
Advanced proficiency in Python and PySpark / Apache Spark
Hands-on experience with cloud platforms and data lake technologies (e.g. Databricks Delta Lake)
Strong SQL skills including complex queries joins and performance optimization
Experience building data pipelines from multiple financial data sources
Familiarity with workflow orchestration tools (e.g. Airflow or similar)
Experience with CI/CD tools such as Git Jenkins or Azure DevOps
Cloud certification (e.g. AWS Certified Cloud Practitioner or equivalent)
Preferred Qualifications
Experience with large-scale financial risk systems and analytics platforms
Background in designing data architectures and maintaining data dictionaries
Experience working in Agile environments using tools such as JIRA or Confluence
Exposure to modern data governance and compliance practices
Skills & Competencies
Strong analytical and problem-solving abilities
Excellent communication skills with both technical and non-technical stakeholders
Ability to work collaboratively with cross-functional teams
High attention to detail and commitment to data quality
Proactive self-driven mindset with continuous learning approach
Focus on regulatory compliance data governance and innovation
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Senior IC
Job Title: Senior Data EngineerLocation: New York NY 10020Position SummaryWe are seeking a highly experienced Senior Data Engineer to support the design development and optimization of large-scale data platforms within a financial services environment. This role focuses on building robust data pipel...
Job Title: Senior Data Engineer
Location: New York NY 10020
Position Summary
We are seeking a highly experienced Senior Data Engineer to support the design development and optimization of large-scale data platforms within a financial services environment. This role focuses on building robust data pipelines enabling advanced credit risk analytics and ensuring compliance with regulatory standards.
The ideal candidate will bring deep expertise in credit and counterparty risk modern data engineering technologies and cloud-based data platforms while collaborating closely with cross-functional teams including risk quant and compliance stakeholders.
Key Responsibilities
Data Architecture & Engineering
Lead architecture and technical design discussions for credit risk data platforms
Design and implement scalable batch and streaming data pipelines using PySpark
Build ingestion frameworks for structured and unstructured data from upstream systems into cloud storage (e.g. S3/ADLS)
Implement data processing workflows using Medallion Architecture (Bronze Silver Gold layers)
Data Processing & Optimization
Develop and optimize transformation logic for large-scale financial datasets
Ensure high data quality auditability and regulatory compliance
Apply optimization techniques such as partitioning indexing and schema evolution
Model and optimize risk metrics (e.g. PD LGD EAD EPE PFE CVA) for analytics and reporting
Platform Integration & Operations
Integrate with external risk engines and support orchestration of complex batch processes
Ensure platform reliability observability and data lineage tracking
Implement and maintain security standards (IAM encryption authentication protocols)
Troubleshoot production issues and provide ongoing support
Collaboration & Documentation
Collaborate with data scientists risk analysts and business stakeholders
Contribute to API design and data contracts for internal and external consumers
Maintain comprehensive technical documentation for audit and compliance purposes
Participate in Agile development processes and ceremonies
Required Qualifications
12 years of experience in data engineering or data development
Strong experience within financial services particularly credit or counterparty risk
Expertise in regulatory frameworks such as Basel III/IV IFRS 9 CECL or FRTB
Advanced proficiency in Python and PySpark / Apache Spark
Hands-on experience with cloud platforms and data lake technologies (e.g. Databricks Delta Lake)
Strong SQL skills including complex queries joins and performance optimization
Experience building data pipelines from multiple financial data sources
Familiarity with workflow orchestration tools (e.g. Airflow or similar)
Experience with CI/CD tools such as Git Jenkins or Azure DevOps
Cloud certification (e.g. AWS Certified Cloud Practitioner or equivalent)
Preferred Qualifications
Experience with large-scale financial risk systems and analytics platforms
Background in designing data architectures and maintaining data dictionaries
Experience working in Agile environments using tools such as JIRA or Confluence
Exposure to modern data governance and compliance practices
Skills & Competencies
Strong analytical and problem-solving abilities
Excellent communication skills with both technical and non-technical stakeholders
Ability to work collaboratively with cross-functional teams
High attention to detail and commitment to data quality
Proactive self-driven mindset with continuous learning approach