Maltem Asia is seeking a Data Engineer for a Banking Client based in Singapore.
The Data Engineer will support the design and implementation of Quantexa-based solutions for Financial Crime (AML/Fraud). The role will bridge business requirements and data/technology teams, translating risk and compliance needs into scalable data-driven solutions.
Responsibilities:
- Gather and translate business requirements (AML, Fraud, KYC) into functional and data specifications
- Define entity resolution, matching and network linking logic aligned to business use cases
- Perform data analysis and mapping across multiple source systems (customer, account, transaction)
- Design logical data pipelines (ingestion, standardization, matching, network generation, scoring)
- Collaborate with Data Engineers to ensure feasibility and alignment of data transformations
- Support data quality assessment, cleansing rules, and standardization approaches
- Validate outputs including entity resolution results, network generation, and risk scoring
- Assist in UAT, defect triage, and business validation of Quantexa outputs
- Prepare functional documentation (BRD, FRD, mapping documents, data dictionaries)
- Work closely with Compliance, Risk, and Operations stakeholders
Required Skills & Experience:
- 5 to 10 years of experience in Financial Services /Capital Markets / Banking Technology
- Hands-on experience in Quantexa implementation or similar platforms (Actimize, SAS AML, Feature space)
- Relevant alternative experience using Alteryx, Linkurious or DataWalk is an added advantage
- Strong understanding of Entity Resolution and data matching techniques
- Understanding of customer and transaction data models
- Knowledge of network / graph-based analytics concepts
- Solid SQL skills (joins, aggregations, data validation)
- Good understanding of data engineering concepts (ETL pipelines, data modeling, data quality)
- In-depth understanding of Apache Spark architecture, RDDs, DataFrames, and Spark SQL
- Strong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Pig, etc)
- Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration using Kubernetes
- Proficiency in programming languages commonly used in data engineering, such as Spark, Python, Scala, or Java
- Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
- Experience with Grafana, Prometheus, Splunk will be an added benefit
- Experience integrating and working with Elasticsearch for data indexing and search applications
- Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization
- Experience with distributed computing, parallel processing, and working with large datasets
- Proficient in performance tuning and optimization techniques for Spark applications and Elasticsearch queries
- Strong problem-solving and analytical skills with the ability to debug and resolve complex issues
- Familiarity with version control systems (e.g., Git) and collaborative development workflows
- Excellent communication and teamwork skills with the ability to work effectively in cross-functional teams
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services is a plus
