FSRC-R and CT-RCT-PYTHON and SQL-STAFF | ErnstYoung Job

Job Description

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

EY GDS – RCT

Staff – Python & Advanced SQL

Staff – Software Engineering      

Experience: Strictly 2+ years       

Eligibility requirement: 

Candidates must have 2–4 years of relevant professional experience, including hands-on cloud experience. Freshers, candidates with less than 2 years of experience, and candidates without cloud experience are not eligible for this role.

The opportunity

Join a financial crime technology team building data-driven solutions for global financial services clients. This role combines hands-on Python engineering, advanced SQL development, and practical exposure to Financial Crime Compliance processes. You will work with business, compliance, data, and engineering stakeholders to deliver reliable solutions across KYC, AML, Transaction Monitoring, Sanctions, and Fraud.

Role overview

We are seeking a hands-on software engineer with strong Python and advanced SQL skills and relevant Financial Crime Compliance domain experience. The successful candidate will develop and support data-intensive applications, analytical workflows, and rule-based solutions used in customer due diligence, transaction monitoring, sanctions screening, fraud detection, investigations, and regulatory reporting.

Key responsibilities

• Design, develop, test, and maintain Python applications, services, utilities, and data-processing workflows.
• Write complex, optimized SQL for data extraction, transformation, reconciliation, profiling, and investigation use cases.
• Work with large financial datasets, including customer, account, transaction, alert, case, and screening data.
• Translate Financial Crime Compliance requirements into technical designs, data rules, scenarios, and reusable components.
• Support KYC and AML workflows such as customer onboarding, customer risk rating, customer due diligence, enhanced due diligence, and periodic review.
• Contribute to Transaction Monitoring, Sanctions, and Fraud solutions, including rule implementation, threshold logic, alert generation, data validation, and issue analysis.
• Develop APIs, batch jobs, automation scripts, and integration components using Python.
• Perform unit testing, code reviews, debugging, root-cause analysis, and production support.
• Collaborate with compliance SMEs, business analysts, data engineers, QA teams, and client stakeholders in Agile delivery teams.
• Follow secure coding, version-control, documentation, and deployment practices.

Primary skills – must have

• Python: Strong hands-on programming experience, including data structures, functions, object-oriented programming, exception handling, modules, logging, testing, and performance-conscious coding.
• Advanced SQL: Strong command of complex joins, subqueries, common table expressions, window functions, aggregations, query optimization, stored procedures, and data reconciliation.
• Databases: Practical experience with at least one relational database such as PostgreSQL, Oracle, Microsoft SQL Server, or MySQL.
• Data engineering: Experience processing structured data using Python libraries such as pandas and integrating with databases, files, or APIs.
• APIs and services: Working knowledge of REST APIs and Python frameworks such as FastAPI, Flask, or Django.
• Engineering practices: Git, unit testing, code review, debugging, documentation, and CI/CD fundamentals.
• Cloud: Mandatory hands-on experience with at least one cloud platform: Microsoft Azure, AWS, or GCP. Candidates should have practical experience developing, deploying, or supporting data-driven applications or services in a cloud environment.

Good to have

• Experience with PySpark or distributed data processing.
• Familiarity with orchestration or ETL tools such as Airflow, Azure Data Factory, or equivalent.
• Experience with data visualization or reporting tools such as Power BI or Tableau.
• Understanding of model or rule performance measures, threshold tuning, false-positive reduction, and data lineage.
• Exposure to Agile delivery tools and practices, including Jira or Azure DevOps.

Financial Crime domain knowledge

• KYC / CDD / EDD: Customer onboarding, identity and document checks, customer risk rating, beneficial ownership, periodic review, and remediation.
• AML: Financial crime risk indicators, typologies, alert and case workflows, investigations, escalation, and regulatory reporting concepts.
• Transaction Monitoring (TM): Scenario or rule logic, thresholds, segmentation, alert generation, tuning support, back-testing, and data-quality controls.
• Sanctions: Customer and payment screening, watchlist concepts, potential-match handling, disposition workflows, and screening data quality.
• Fraud: Fraud indicators, rules or models, event and transaction analysis, alert triage, investigations, and case outcomes.

Experience and education

• Experience: Strictly 2–4 years of relevant professional experience in software engineering, data engineering, analytics engineering, or technology delivery.
• Eligibility: Freshers and candidates with less than 2 years of relevant experience will not be considered.
• Cloud experience: Hands-on experience with Microsoft Azure, AWS, or GCP is mandatory.
• Domain experience: Relevant delivery experience in KYC, AML, Transaction Monitoring, Sanctions, or Fraud is good to have.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.

What success looks like

• Production-quality Python and SQL that is accurate, maintainable, secure, and efficient.
• Clear understanding of the business purpose and regulatory context behind the solution being built.
• Strong ownership of assigned deliverables, defects, documentation, and stakeholder communication.
• Ability to explain technical findings and data issues clearly to both engineering and compliance stakeholders.

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