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Job Summary We are looking for a skilled Data Engineer to build, optimize, and scale our modern data platform on AWS. You will play a key role in designing data models and maintaining both real-time streaming and batch processing pipelines. Working closely with business stakeholders, Data Architects, and Data Users, you will ensure our data infrastructure is robust and performant. Beyond reporting, a critical part of your role is to guarantee that the data seamlessly supports advanced analytics, Data Science workflows, and is structured to be future-proof for upcoming AI initiatives. A deep understanding of core financial data domains—such as spending, lending, deposits, and customer domain—is essential to translate business needs into reliable data solutions. Additionally, you will champion robust SDLC practices to ensure stable, automated, and secure pipeline deployments. Key Responsibilities Pipeline Engineering - Design, build, and maintain scalable batch and real-time data pipelines using Apache Spark and Apache Flink. Data Modeling & Documentation - Design, build, and maintain scalable data models tailored for analytical workflows and Lakehouse architectures. Create and maintain comprehensive Entity-Relationship (ER) diagrams to ensure clear structural visibility. Data Catalog & Governance - Actively manage and maintain the Data Catalog and Business Glossary, ensuring clear data lineage, accurate metadata, and alignment between technical terms and business definitions. Data Pipeline SDLC & CI/CD - Apply software engineering best practices to data workflows. Manage version control using Git, write automated tests for data transformations, and build robust CI/CD pipelines to automate the deployment of data pipelines and Airflow DAGs. Business Domain Alignment - Develop a strong understanding of core financial business data domains (including customer profiles, spending behaviors, lending, and deposits) to ensure data pipelines accurately reflect business logic and rules. Reporting & Analytics - Develop, automate, and maintain business-critical reports and dashboards directly from the data platform to support stakeholder decision-making and operational tracking. Data Lakehouse Management - Manage and optimize data storage using Apache Iceberg table formats and Amazon Redshift for analytical workloads. Data Quality Assurance - Implement and maintain data quality checks. Qualifications Experience 3+ years of experience as a Data Engineer, or in a similar data-centric role. Proven track record of end-to-end building, optimizing, and maintaining robust, scalable data pipelines for both batch and real-time workloads. Deep understanding of the Software Development Life Cycle. Strong experience with Git version control, implementing CI/CD pipelines. Strong proficiency in SQL and Python. Proven hands-on experience in conceptual, logical, and physical data modeling, including generating ER diagrams. Proven hands-on experience with the AWS ecosystem (S3, IAM, Glue, Redshift, ECS, or EMR). Solid experience with distributed data processing frameworks (Apache Spark is a must). Experience working with Reporting tools, Data Catalogs, and maintaining a Business Glossary. Nice-to-Have Prior experience working in Banking, Fintech, or Financial Services with a strong grasp of liquidity, lending, or deposit data. Familiarity with specialized AWS analytics, streaming, and integration services (e.g., AWS Lake Formation, Amazon Glue, Amazon Redshift, or AWS Lambda). Experience implementing Lakehouse architectures (Apache Iceberg) or real-time stream processing (Apache Flink). AI & MLOps: Understanding of MLOps concepts, feature stores, and integrating data pipelines with AWS SageMaker to support Data Scientists. Benefits: Hybrid Working Arrangement World-Class Development Program Performance Bonus Vacation Leave 15 Days + Maternity Leave MacBook Provided Housing Loan Life Insurance/ Health Insurance/ Dental Care Jetts Fitness (Corporate rate and privilege) Opportunity to be a part of team that drives Thailand Digital Economy (The contributor of the great impact to millions of Thai people through digital platforms e.g. PaoTang App. and Krungthai Next App) Working Location : The ParQ ชั้น 5, 9-10 ติดกับ MRT สถานีศูนย์การประชุมแห่งชาติสิริกิติ์ ทางออกที่ 2
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.