Team Intro USDS Financial Crime Compliance is responsible for overseeing and operationalizing all aspects related to Anti-Money Laundering (AML) and Sanctions Compliance activities related to TikTok USDS's operations. Collaborating cross-functionally with stakeholders across the U.S. and globally, we address complex and cutting-edge challenges, which include the identification of money-laundering, terrorist financing and sanction violations. We seek a highly motivated, experienced, and dynamic professional to join our team. In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time. Position Overview As a Data Engineer, you will play a crucial role in leveraging machine learning, analytics, and visualization techniques to enhance our organization's capabilities in detecting and preventing financial crimes. You will be responsible for analyzing large datasets, developing machine learning models, and creating visualizations to identify patterns, anomalies, and potential risks related to money laundering, fraud, and sanctions violations. This role offers an exciting opportunity to apply advanced technology solutions to combat financial crime and ensure regulatory compliance. Responsibilities: - The perfect candidate will leverage strong data infrastructure and architecture skills to design and maintain efficient scalable data systems, strong operational skills to drive efficiency and speed, and apply strategic vision and project management expertise to ensure timely execution of complex data engineering projects that improve FCC compliance processes - Create and maintain technical documentation, such as data dictionaries, data flow diagrams, and system documentation, to ensure efficient and effective data management and analysis - Design, implement, and operate large-scale end-to-end distributed systems to perform data discovery using scalable, reusable, and configurable frameworks / methodologies - Define metrics and create / maintain dashboards for measuring and reporting key performance indicators - Build and manage data inventories and data flow mappings by collecting and aggregating datasets from multiple data source systems - Continuously improve the integrity of data pipelines to provide a comprehensive data service - Build reliable and fault resilient data pipelines
Minimum Qualifications - Bachelor's degree in Computer Science, Data Science, Statistics, or a related field. - 2+ years of experience in data analysis, machine learning, or data visualization, with a focus on financial crime compliance or related domains. - Strong analytical skills and the ability to work with large datasets to extract actionable insights. - Demonstrate strong critical thinking skills, with the ability to analyze complex problems, evaluate information objectively, and communicate well-reasoned conclusions based on evidence and logic. Preferred Qualifications: - 5+ years experience programming and debugging Python, SQL, PySpark - 2+ years of proficiency in distributed data processing using Big Data technologies like Spark/Scala, Java, Hadoop/HDFS/AWS/S3, Cassandra and Kafka - Ability to effectively collaborate with cross-regional teams of diverse backgrounds to meet strategic and tactical objectives as well as serving as an individual contributor - Ability to stay curious about new developments in the industry and learn & adapt quickly - Strong understanding of containers and their orchestration (Kubernetes), and significant experience with public/hybrid/private cloud - Strong background in algorithms and data structures - Ability to communicate effectively, both written and verbal, with technical and non-technical partners - Ability to deliver consistent high quality results while working in a fast paced environment - Passionate, curious, and seeking to tackle every day problems with innovation
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