เงื่อนไขการค้นหา

ทักษะ:

ETL, Big Data

ประเภทงาน:

งานประจำ

• Pipeline Development: Support the design and maintenance of scalable ETL/ELT pipelines for structured and unstructured datasets..
• AI Data Readiness: Assist in building data ingestion flows for Vector Databases and supporting RAG (Retrieval-Augmented Generation) architectures..
• Data Modeling: Contribute to the creation of robust data models and Feature Stores that serve both traditional analytics and machine learning workloads..
• Data Quality & Operations.
• Validation & Cleaning: Implement automated scripts to ensure high data integrity, reliability, and performance across the platform..
• Cloud Optimization: Assist in monitoring cloud resource usage (GCP/AWS) to ensure cost-efficiency and low-latency data access..
• Engineering Collaboration: Work closely with senior engineers to document data lineage and ensure the architecture is built for long-term scalability..
• Experience: Entry-level to 2 years of experience in data engineering, backend development, or a related technical internship.
• Portfolio: Demonstration of coding ability through a Github repository or a portfolio of data projects (e.g., building a personal API, a data scraper, or a small-scale ETL project)..
• Education: Bachelor s degree in Computer Engineering, Computer Science, Statistics, or a related technical field..
• Python: Solid foundation in writing clean, modular Python code.
• SQL: Proficiency in writing and optimizing complex queries for data analysis.
• Cloud Knowledge: Familiarity with at least one major cloud provider (GCP or AWS) and basic understanding of services like BigQuery, Redshift, or S3.
• AI Awareness: Interest in how data engineering supports AI; basic knowledge of unstructured data or vector search is a plus..
• What We Offer.
• Hands-on Multi-Cloud Experience: Get direct exposure to large-scale data environments on GCP/AWS and modern orchestration tools..
• Innovative Tech Stack: Work with cutting-edge tools at the intersection of Big Data and AI, including Vector DBs and automated data quality frameworks..
• Growth & Mentorship: A supportive environment where you will learn from senior engineers and have a clear path for professional development..
• Impactful Work: See your data pipelines directly power real-time marketing decisions and AI-driven products..
• If you re ready to build the data foundation for the next generation of AI, apply now!.

ทักษะ:

ETL, Compliance, SQL

ประเภทงาน:

งานประจำ

• Design, implement, and manage end-to-end data pipelines architectures.
• Configure and maintain data ingest workflows (ETL) across several production systems.
• Transform data into Data Mart, Data Model that can be easily analyzed.
• Ensure data accuracy, high usability, timely availability, and strong performance.
• Demonstrate a hands-on development mindset with a willingness to troubleshoot and solve complex problems.
• Ensure compliance with data governance and security policies.
• Minimum of 3 years of work experience as a Data Engineer.
• Strong SQL skills with knowledge of NoSQL tools and languages.
• Strong proficiency in Python scripting.
• Experience with AWS Cloud Data Platform services such as S3, Redshift, Glue, Step Functions, and Lambda.
• Experience with other cloud data platforms such as GCP or Azure is an advantage.
• Experience working on Big Data platform is an advantage.
• Strong business understanding, with the ability to identify business problems, define business goals, and locate relevant data.

ประเภทงาน:

งานประจำ

• The Senior Data Engineer position plays a key role in designing, developing, and managing cloud-based data platforms, as well as creating data structures for high-level data analysis, and works with business and technical teams to ensure that data management is appropriate and supports organizational goals.
• Responsible for the design, construction, and maintenance of optimal and scalable data pipeline architectures on cloud platforms (e.g., GCP, AWS, Azure).
• Oversee the development and management of complex ETL/ELT processes for data ingesti ...
• Author and optimize advanced, high-performance SQL queries for complex data transformation, aggregation, and analysis.
• Leverage the Python programming language for automation, scripting, and the development of data processing frameworks.
• Administer and optimize cloud-based data warehouse solutions and associated data lakes.
• Collaborate professionally with data scientists, analysts, and key business stakeholders to ascertain data requirements and deliver effective technical solutions.
• Provide mentorship to junior engineers and champion the adoption of data engineering best practices throughout the organization.
• Bachelor s degree or higher in Computer Science, Information Technology, Engineering, or a related field.
• At least 5 years of experience working in a data engineering or related position.
• Proficient in advanced SQL, including query optimization and performance tuning.
• Experienced in managing and designing architecture on at least one major cloud platform (Google Cloud Platform, AWS, or Azure).
• Skilled in using Python for data processing and advanced pipeline development.
• Experienced with tools and technologies for data ingestion, connectivity, and management.
• Deep understanding of data modeling principles, data warehousing methodologies, and modern data architecture.
• Excellent analytical and problem-solving skills.
• Communication and teamwork skills.
• Ability to plan and manage tasks effectively.

ทักษะ:

ETL

ประเภทงาน:

งานประจำ

• Design and deliver cloud-native banking solutions on AWS.
• Build rapid prototypes and live demos for client engagements.
• Integrate AI and data capabilities into core banking platforms.
• The ideal candidate combines strong engineering depth, banking domain expertise, and the ability to translate ideas into working solutions quickly.
• Core Banking Transformation.
• Lending / loan origination and servicing.
• Credit decisioning systems.
• Payments, deposits, and investment platforms.
• Legacy cloud-native (rehost, replatform, refactor).
• Monolith microservices / event-driven architecture.
• Translate business requirements into scalable, secure, production-ready architectures.
• Client-Facing Demo & Prototyping (Critical Requirement).
• Build end-to-end functional prototypes and demos on AWS to showcase solutions.
• Working applications.
• API-driven services.
• AI/data-driven proof-of-value solutions.
• Solution workshops.
• Pre-sales discussions.
• Live demo sessions with business and technology stakeholders.
• Expected capability: Build a working demo (e.g., AI-powered credit workflow or customer 360 solution) within short timelines.
• AWS Cloud Engineering (Hands-on Senior Level).
• Architecture & Design.
• Microservices and event-driven patterns.
• API-first integration models.
• High availability, scalability, and resiliency.
• Security and regulatory compliance.
• Core AWS Stack.
• Compute: Lambda, ECS/EKS, EC2.
• Data: S3, Aurora/RDS, DynamoDB.
• Integration: API Gateway, Step Functions, EventBridge.
• Observability: CloudWatch and monitoring frameworks.
• DevOps & Automation.
• Infrastructure-as-Code (Terraform / CloudFormation / CDK).
• CI/CD pipelines and automated deployment.
• Ability to stand up environments quickly for demos and delivery.
• AI & Data Capabilities in Banking (Strong Preference).
• Data Engineering.
• Data lakes (S3-based architectures).
• ETL / ELT pipelines.
• Customer, transaction, credit, and risk datasets.
• Machine Learning & Analytics.
• Credit scoring and underwriting.
• Fraud detection / AML.
• Customer segmentation and personalization.
• GenAI & Modern AI.
• RAG-based solutions (e.g., document processing, KYC).
• AI assistants for banking users.
• AWS Bedrock / SageMaker / equivalent.
• LLM orchestration and vector search.
• Real-Time Decisioning.
• Real-time credit approvals.
• Fraud detection and alerts.
• Client Engagement & Thought Leadership.
• Act as a trusted technical advisor to financial services clients.
• Architecture discussions.
• Solution design workshops.
• Pre-sales and technical solutioning.
• Core banking modernization.
• Cloud + AI transformation.
• Bridge business, data, and technology stakeholders effectively.
• Banking technology / financial services IT.
• Core banking systems (lending, payments, deposits, investments).
• Core banking modernization or migration programs.
• Large-scale transformation initiatives.
• Cloud-native architecture and development.
• Microservices / distributed systems.
• Strong hands-on programming experience in Python (preferred), with the ability to build cloud-native applications, data pipelines, and rapid client-facing prototypes.
• Build working demos / PoCs under time pressure.
• Operate effectively in client-facing roles..
• Preferred Qualifications.
• Temenos, Mambu, Finacle, FLEXCUBE, nCino.
• Data engineering and analytics platforms.
• ML / GenAI use cases integrated into business workflows.
• Event-driven architecture.
• API-first ecosystems.
• Domain-driven design (DDD).
• AWS Certifications (Associate / Professional).
• Profile We Are Targeting.
• Hands-on builder (not a pure architect or program manager).
• Able to design, build, and demo solutions live to clients.
• Strong banking domain credibility.
• Bridges cloud + core banking + AI/data.
• Comfortable in pre-sales + delivery hybrid environments..