About
Quantexa Data Engineer supporting SMBC's AML Market applications and data services. Purpose: design, build, and maintain scalable data pipelines and shared libraries that ensure data availability, quality, reliability, and regulatory compliance for Anti-Money Laundering obligations. Scope and impact: enterprise-scale AML data engineering on Azure (Data Factory Gen2, Databricks), Elasticsearch, and MS SQL Server, enabling faster time to market through reusable capabilities, improving platform health and performance, and reducing compliance risk by strengthening data integrity and security. Reporting structure: reports to AML Market Technology leadership and partners closely with Technology, Product, and Business stakeholders. Principal Duties And Responsibilities
Design, develop, and optimize robust data pipelines and ETL processes for AML data domains. Build reusable libraries and shared capabilities to accelerate delivery across Data Engineering teams. Evolve platform capabilities and maintain overall platform health, performance, and reliability. Collaborate with cross-functional stakeholders to translate data requirements into solutions. Ensure data quality, integrity, and security across all data systems and workflows. Monitor, troubleshoot, and continuously improve data workflows, SLAs, and performance. Automate validation, testing, and deployment processes to increase delivery speed and consistency. Develop and integrate services using Spark and Scala or Java or Python; implement RESTful APIs with JSON; support Tomcat-hosted services and Elasticsearch indexing/queries. Apply Agile practices; manage work using Jira and documentation using Confluence. Operate within DevOps tooling and pipelines (git, Azure DevOps, Jenkins). Stay current with emerging data engineering technologies and trends; propose improvements. Communicate proactively with business users, support teams, vendors, and stakeholders to ensure high customer satisfaction. Position Specifications
Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or related field. 7+ years in data engineering or closely related roles. Technical Skills: Highly proficient in Scala, Python, and SQL; hands-on with Spark; experience with Apache Airflow. Azure cloud data platforms: Azure Data Factory Gen2, Azure Databricks, Azure-hosted databases. Elasticsearch experience; RESTful APIs with JSON; Tomcat application services. Advanced SQL on relational databases, preferably MS SQL Server and Azure-hosted databases. Strong ETL tooling knowledge and data modeling skills. DevOps lifecycle experience: git, Azure DevOps, Jenkins. Agile methodologies; Jira and Confluence proficiency. Linux/Unix environment familiarity. Professional Competencies: Strong analytical and diagnostic skills for troubleshooting complex systems. Ability to design scalable data architectures and shared frameworks that improve time to market. Clear, proactive communication with technical and non-technical stakeholders. Focus on data quality, integrity, security, and SLA adherence. Our positions are open to all, regardless of their human characteristics or conditions regarding ethnicity, gender, sexual orientation, accessibility, cultural, social, or any other factor. As well, will always be open to people with disabilities. EOE, including Disability/veterans.
Languages
- English
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