
The Goldman Sachs Group, Inc
Financial Crimes Compliance Engineering – Data Analyst, FCC Engineering
필수 스킬
AWS
The Goldman Sachs Compliance Division prevents, detects and mitigates regulatory and reputational risk across the firm, and helps to strengthen the firm's culture of compliance. As an independent control function and part of the firm's second line of defense, Compliance:
- Assesses the firm's compliance, regulatory and reputational risk
- Monitors for compliance with new or amended laws, rules and regulations
- Designs and implements controls, policies, procedures and training
- Conducts independent testing
- Investigates, surveils and monitors for compliance risks and breaches
- Leads the firm's response to regulatory examinations, audits and inquiries
Compliance Engineering empowers these activities by building and operating a suite of software platforms and applications. We are a team of more than 300 engineers and scientists who work on the most complex, mission-critical problems. We have access to the latest technology and to massive amounts of structured and unstructured data. We leverage modern frameworks to build responsive and intuitive UX/UI and Cloud applications, incorporating cutting-edge AI and efficient processes to drive them.
Compliance Engineering user base spans thousands of users globally. We partner with Compliance Officers across divisions, to fully understand the financial products, business strategies, and regulatory regimes. This knowledge enables us to build long-lasting software solutions, and to innovate with a purpose.
Compliance Engineering is looking to fill a Data Analyst role within FCC Engineering.
How will you fulfil your potential
The Financial Crimes Compliance (FCC) Engineering, under Global Compliance, is responsible for Architecture, Design, Development and Implementation of best-in-class software solutions to ensure compliance with regulatory mandates and mitigate any reputational and related risks to the firm. A key component of second line of defense for the firm, FCC Engineering builds and maintains technology solutions for Know Your Customer (KYC), Anti-Money Laundering Detection (AML Monitoring), Sanctions Screening, Anti-Bribery and Corruptions and all other related FCC areas.
With constantly changing regulatory and technology landscape, FCC engineering strives to stay ahead of the curve through continuous innovation.
As a Data Analyst within FCC engineering, you will be a hands-on contributor responsible for developing and supporting scalable FCC data solutions. You will work closely with architects, product partners and compliance stakeholders to implement end-to-end FCC data strategies, curate high quality single source of truth databases and support the development of ML/AI capabilities on AWS/Snowflake and related Cloud ecosystem. This role is implementation heavy and expects strong engineering ownership from design through production support.
In this role, you will contribute to innovation and will be responsible for, among other related functions, the following:
- Contributes to the design, development, and maintenance of data pipelines and ETL processes for financial crimes compliance applications, ensuring data synchronization with production systems.
- Assist in the development and maintenance of data products for the “Single Source of Truth” financial crimes compliance data fabric, including on-prem and cloud storage and real-time/batch compute.
- Support the implementation of storage and compute separation with true data federation to avoid data duplication, eliminate compute resource conflict and ensure cloud-native architecture.
- Assist in the development and implementation of innovative AI/ML solutions for proactive management of financial crimes compliance risks.
- Support cloud migration strategies for financial crimes compliance applications.
- Support the implementation and monitoring of controls for completeness, accuracy and timeliness of data, integrations and applications availability and performance.
Required Qualifications
- A bachelor’s or master’s degree in computer science, engineering, data science, or a similar field of study.
- 5-9 years of hands-on experience with data engineering, ETL development, and data warehousing.
- Solid experience with Big Data/Cloud Engineering and distributed processing (Batch and streaming concepts).
- Hands-on experience with AWS (Data platforms, Compute, Storage, Security, Process Orchestration and CI/CD).
- Proficient hands-on experience with Snowflake including Snowpark, Snow SQL, RBAC, Masking and Snowsight.
- Experience in implementing controls framework for enterprise-wide complex data and applications landscape.
- 2-4 years of experience in financial crimes compliance or a related data-intensive field within a global organization.
- Hands-on experience with enterprise solutions development using Python for data manipulation and scripting.
- Familiarity with Large Language Models (LLMs) and their potential application in data analysis or compliance.
- Excellent communication, negotiation and stakeholder engagement skills to create consensus and build collaborative relationships.
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Goldman Sachs 소개

Goldman Sachs
PublicThe Goldman Sachs Group, Inc. is an American multinational investment bank and financial services company. Founded in 1869, Goldman Sachs is headquartered in the Battery Park City neighborhood of Manhattan in New York City, with regional offices in many international financial centers.
45,000+
직원 수
Lower Manhattan
본사 위치
$80B
기업 가치
리뷰
2개 리뷰
2.9
2개 리뷰
워라밸
2.5
보상
3.0
문화
2.0
커리어
4.0
경영진
2.5
45%
지인 추천률
장점
Amazing career growth opportunities
Chill management at some locations
Work-life balance valued in certain roles
단점
Toxic workplace culture
Codependent atmosphere
Confusing interview process
연봉 정보
20,304개 데이터
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Analyst
6,923개 리포트
$112,993
총 연봉
기본급
$97,759
주식
-
보너스
$15,234
$77,583
$166,892
면접 후기
후기 4개
난이도
3.5
/ 5
소요 기간
21-35주
경험
긍정 0%
보통 75%
부정 25%
면접 과정
1
Application Review
2
HR Screen/HireVue
3
Recruiter Screen
4
Superday/Panel Interview
5
Final Decision
자주 나오는 질문
Behavioral/STAR
Technical Knowledge
Culture Fit
Past Experience
Case Study
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