Banking Analytics Engineer â Automation & Reporting
āļāļĩāđāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ)Banking Analytics Engineer â Automation & Reporting
Work at Infinitas By Krungthai Co., Ltd.
Job Summary
The Data Innovation Team at Infinitas by Krungthai is a centralized data enabler designed to serve both the digital and traditional arms of Krungthai Bank. We focus on providing cutting-edge data solutions, empowering teams with actionable insights, and driving business transformation. We are looking for an Analytics Engineer to join our team, focusing on automation, reporting, and DataOps. This key role will help streamline data pipelines, automate
reporting workflows, and provide high-quality, data-driven insights to enhance decision- making.
Our team responsibilities
o Automation of Analytics Pipelines
- Develop and Maintain Automated Data Pipelines: Build and maintain robust data pipelines for reporting and analytics using cloud-native technologies such as AWS Glue, Redshift, and Lambda.
- Streamline Automation Frameworks: Ensure the high availability, performance, and cost-efficiency of data workflows by adhering to âZero Ops by Designâ principles, ensuring that data pipelines run seamlessly with minimal manual intervention.
- Timely, Accurate Reporting: Automate reporting processes to ensure consistent, accurate, and timely delivery of business insights.
o Advanced Reporting & Analytics
- Reporting Systems Design & Optimization: Design, implement, and optimize reporting systems that deliver actionable insights to key business stakeholders.
- BI Tools & Dashboards: Use visualization tools such as Tableau, Grafana, and AWS Quick Sight to create dynamic, self-service dashboards and reports, empowering teams to make data-driven decisions.
o Data Modeling and Schema Management
- Develop Robust Data Models & Schemas: Design and maintain data models and schemas that support analytics, reporting, and operational needs.
- Single Version of Truth: Ensure consistency, accuracy, and reliability by establishing a "single version of truth," providing a consistent data framework across the organization.
o Quality-as-a-Service Development
- Build Scalable Quality Solutions: Design and maintain "Data/AI Quality-as-a-Service" solutions to monitor data drift, analyze performance metrics, and detect data issues early in the process.
- Zero-Ops Design for Quality Monitoring: Ensure the high availability and performance of quality solutions while aligning with zero-ops design principles, minimizing operational overhead.
o Cross-Functional Collaboration
- Collaborate with Teams: Work closely with data scientists, analysts, and application developers to integrate data solutions seamlessly into their workflows, enabling advanced analytics and enhancing decision-making capabilities.
o Compliance & Security
- Ensure Data Security & Compliance: Uphold data security and privacy standards while ensuring all solutions comply with banking regulations and industry governance requirements.
- Governance Standards: Maintain rigorous governance practices for data access, privacy, and security across all automated reporting systems.
o Continuous Improvement
- Technology Advocacy: Stay informed about emerging trends in cloud data engineering, automation, and analytics. Advocate for the adoption of new technologies that can enhance system capabilities and maintain a competitive edge.
- Drive Continuous Improvement: Continuously refine processes and solutions to ensure they remain optimized for both performance and cost.
Qualification:
- Essential Skills & Experience
o Bachelor's degree in Computer Science, Engineering, Business Information System or a related field.
o 2+ years of experience in data engineering, automation, or analytics engineering, focusing on reporting and business intelligence in the financial or banking sector.
o Expertise in cloud platforms (AWS preferred) and technologies such as AWSGlue, Redshift, Lambda, and S3.
o Experience with BI/Visualization tools (e.g., Tableau, Grafana, AWS QuickSight).
o Strong understanding of data modeling principles, ETL/ELT processes, and creating data schemas for reporting and analytics.
o Proficiency in SQL, Python, or other relevant programming languages.
o Familiarity with "Zero Ops by Design" principles and automation frameworks.
- Preferred Skills
o Knowledge of financial regulations and their impact on data governance and reporting in the banking sector.
o Experience in building and maintaining "Data/AI Quality-as-a-Service" solutions for monitoring and ensuring data quality.
o Familiarity with DevOps practices and CI/CD pipelines for analytic engineering solutions.
o Experience in setting up and maintaining high-performing, scalable reporting systems.
o Understanding of advanced analytics and machine learning concepts.
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