Senior Python / PySpark Data & AI Analytics Engineer - Financial Crime Plat at TymeX

Ho Chi Minh City, , Vietnam -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

0.0

Posted On

22 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, SQL, Databricks, Snowflake, Cloud-Based Data Platforms, Data Quality Checks, ETL Processes, Agentic AI, Machine Learning, Secure Coding Practices, Git-Based Version Control, DevOps, CI/CD, Entity Resolution, Data Streaming

Industry

Software Development

Description

About TymeX TymeX is Tyme Group's Technology & Product Development Hub with a global mission to become serial bank builders. Our Financial Crime platform plays a critical role in protecting customers and the bank from fraud, money laundering, and other financial crimes using advanced data, analytics, AI, and automation. About the Role We’re looking for a Senior Python / PySpark Data & AI Analytics Engineer to join our Financial Crime team. You’ll be part of the group responsible for building and maintaining high-performance data pipelines that drive fraud detection, entity resolution, and compliance analytics across Tyme’s ecosystem. You’ll work with large, complex datasets in Databricks, integrate data from multiple systems in real time and batch, and help design the data foundation for real-time risk detection and case management. Key Responsibilities: Build and optimize data ingestion pipelines using Python and PySpark to collect and transform data from multiple sources (transactions, KYC, AML, authentication, devices, logs, etc.). Proficiency in SQL (PostGres preferred) Design and maintain data model that support Financial Crime/Fraud detection, profiling, and entity resolution. Implement data quality checks and ensure data reliability across environments. Collaborate closely with Data Scientists, Analysts, Compliance, Operations and our Product/Feature teams to operationalize models and rules. Utilize jobs, workflows, APIs and streaming to manage large-scale data processing workloads. Integrate with external systems (e.g. sanctions, ID&V, biometrics and authentication systems) to enrich risk and identity data. Support automation and monitoring of ETL processes to improve operational efficiency. Must-Have: Bachelor’s degree. 5+ years of experience Strong skills in Python, PySpark, Scala and Advanced SQL (preferably PostGres) Hands-on experience with Databricks, Snowflake, Fabric or similar Experience working with structured and unstructured data in a production environment. Experience with Agentic AI, MLFlow, ML models, Eval Secure Coding practices – testing/QA Comfortable with cloud-based data platforms (preferably AWS). Good communication skills in English — able to collaborate with cross-functional teams in an international environment. Proficiency in working with Text, Delta, Parquet, JSON, CSV, and XML data formats. Working knowledge of Spark structured streaming. AWS infrastructure experience, specifically working with S3. Solid understanding of git-based version control, DevOps, and CI/CD. Experience of working on Atlassian stack a plus. Knowledge of common web API frameworks and web services. Strong teamwork, relationship, and client management skills, and the ability to influence peers and senior management to accomplish team goals. Willingness to embrace modern technology, best practice, and ways of work. Nice to Have: Experience in Financial Crime/AML, KYC, or fraud detection systems. Familiarity with Entity Resolution frameworks (e.g., Quantexa, Sensing, open source Entity Resolution tools). Experience with data streaming frameworks (Kafka, Spark Streaming, MQ). Be part of a mission-driven team tackling real-world financial crime problems. Work with modern data tech stack with Agentic AI and advanced ML. Hybrid working model with flexible hours. International and collaborative culture — working with colleagues across Vietnam, Singapore, Philippines and South Africa. Competitive salary, performance bonuses, and learning support.

Responsibilities

Build and optimize data ingestion pipelines using Python and PySpark to collect and transform data from multiple sources. Collaborate closely with Data Scientists, Analysts, Compliance, Operations, and Product/Feature teams to operationalize models and rules.