Senior Data Integration Engineer - Credit Risk Experience (Only Independent Candidates)

Major 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, 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, 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.

Required Qualifications:

• 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, 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.