Senior Data Integration Engineer - Credit Risk

Accord Technologies Inc; New York (NY); Hybride; USD 140 000 - 180 000; Plein temps

Résumé du poste

Accord Technologies Inc in New York, NY seeks a Senior Data Integration Engineer with Credit Risk experience to own end-to-end data pipelines for counterparty risk analytics. The role focuses on ingesting data from multiple upstream systems, transforming and integrating it for CCR exposure analytics (EPE, PFE, EAD) and regulatory reporting.

You will work with EM/CRR users and business analysts to translate requirements into robust, scalable data solutions using Databricks, PySpark, and cloud

Qualifications

• 10+ years of Python and advanced SQL experience focusing on data transformation and analytics.
• 5+ years in data engineering, data integration, or enterprise data management in financial services.
• 3+ years in a VP level or equivalent senior role supporting risk or exposure platforms.
• 3+ years with Databricks and PySpark for large-scale ETL and data integration.

Responsabilités

• Design, develop, and optimize scalable data pipelines for CCR exposure analytics.
• Implement ETL/ELT solutions across batch and streaming processes with data lakes and data warehouses.
• Integrate trading and derivatives data into CCR calculation and reporting platforms.
• Collaborate with EM/CRR users and analysts to understand exposure-related requirements.

Connaissances

Python

SQL

PySpark

Outils

Databricks

Snowflake

PowerBI

Airflow

Jenkins

GitLab CI

Azure

Kafka

Description du poste

Senior Data Integration Engineer - Credit Risk Experience

New York, NY (Need Onsite day 1, Hybrid 3 days from office)

Position type: W2 contract

Job Description

We are seeking a senior Counterparty Credit Risk (CCR) Data Integration Engineer to assume end to end accountability for stable, resilient, and observable data integration pipelines across CCR technology platforms.

The Role

Responsibilities

• Design, develop, maintain and optimize scalable data pipelines to ingest, transform, and integrate counterparty, trade, collateral and market data from upstream systems (Systems Of Record) for CCR exposure analytics (e.g., EPE, PFE, EAD, sensitivities).
• Implement robust ETL/ELT solutions for structured and semi structured data across batch and streaming processes using enterprise data platforms (e.g., data lakes, data warehouses, and distributed processing frameworks).
• Support integration of trading and derivatives data (e.g., exposures, collateral, netting, margin) into CCR calculation and reporting platforms.
• Partner with EM and CRR users and business analysts to understand business requirements related to exposure, PFE, EAD, and stress testing.
• Enable accurate, consistent, validated CCR data for EPE/PFE calculations, limit monitoring, what if pre-trade intraday analysis, and regulatory and HO reporting.
• Deliver timely enhancements to ETL pipelines, CCR data models, data lineage, and controls aligned with the EM users and regulatory expectations (e.g., Basel regulations).
• Implement data quality checks, reconciliations, and monitoring to ensure completeness, accuracy, and timeliness of CCR data (Data governance and compliance).
• Proactively identify and execute opportunities for process standardization/optimization, tooling enhancements, and operational simplification across the CCR application stack.
• Lead by example in technical documentation, knowledge transfer, implementation standards, data management, and control frameworks, reducing dependency on key individuals and ensuring service quality, productivity, and continuous skill development.
• Proactively investigate, identify root cause of recurring incidents, and resolve data issues across upstream and downstream systems, working with IT and business stakeholders.
• Demonstrate a continuous improvement mindset through their example, with a focus on reducing incidents, manual interventions, and operational risk, and improving turnaround time for incidents.
• Drive root cause analysis (RCA) for impactful incidents (Calculation breaks, data validation/reconciliation issues), ensuring recurring issues are eliminated through permanent fixes rather than short term workarounds.
• Champion automation and self healing for batch monitoring, data validation, reconciliations, and recovery processes for production as well as lower testing environments.
• Partner with BAU Ops and Infrastructure teams to improve data transparency, and observability, alerting, capacity planning, and resilience of CCR platforms.
• Ensure DR/BCP readiness for EM critical systems, including regular/annual testing and documented recovery procedures.
• Work closely with EM and CRR users, Business Analysts, Risk Analytics, and Technology teams to deliver end to end data solutions.
• Take initiative and come up with enhancement proposals and lead platform migrations, and strategic data initiatives.
• Support audit, regulatory, and ad hoc data requests related to counterparty credit risk

Requirements

• 10+ years of strong programming experience in Python and advanced SQL, with a focus on data transformation and analytics.
• 5+ years of experience in data engineering, data integration, or enterprise data management within financial services.
• 3+ years in a VP level or equivalent senior role supporting risk or exposure platforms.
• 3+ years of hands on experience with Databricks and PySpark for large scale ETL and data integration.
• Demonstrated experience operating global, offshore leveraged support models in a regulated environment.

Technical & Domain Expertise

• Proficient understanding of Exposure Management and Counterparty Credit Risk concepts, including derivative trade cycles, market data, EPE, PFE, EAD, netting, Collateral/margin management, limits, stress testing, and what if analysis.
• Experience supporting batch intensive and intraday real time risk platforms (Python, PySpark, ETL, Tidal, Stored Procedure, SQL, Snowflake, PowerBI).
• Demonstrated experience designing and managing Databricks, Medallion Architecture, Unity Catalog, Workflows/Orchestration, including complex DAGs and dependencies (Exposure to Astronomer/Airflow is a plus).
• Hands-on experience with Cloud computing and infrastructure (Azure, Data Lake, ADF, Kafka, Spark based distributed compute, other Cloud native technologies).
• Experience implementing CI/CD pipelines using Jenkins, GitLab CI, or Azure DevOps in a data engineering environment.
• Familiarity with regulatory risk data principles and expectations for banking institutions (e.g., aggregation, reconciliation, and auditability of risk data).
• Proven track record taking initiative and driving a technological transformation project with an Agile based application/software development.
• Proven ability to utilize JIRA, Confluence to manage tasks, technical documentation, production incidents, and releases.

Leadership & Soft Skills

• Demonstrated continuous improvement mindset, with the ability to drive cultural change across the engineering team.
• Strong experience collaborating with distributed, multicultural global teams and third party vendors.
• Excellent interpersonal, written and verbal communication skills with the ability to work with both technical and non technical stakeholders, and senior Technology team leads.
• Ability to prioritize and effectively manage multiple tasks under time-critical and regulatory pressure.
• Highly motivated, self-directed individual with the ability to work independently and in team environments.
• Effective presenter capable of articulating high level concepts to strategic execution plans including system/architecture/data flow diagrams, combined with extreme attention to detail.
• Strong analytical, problem solving (breaking ambiguous/large problems to smaller/less complex ones), and decision-making skills.
• Collaborative team player and relationship builder.