Data Engineer (Quantexa_Alteryx_SQL_ELK_DevOps_AWS)

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