Data Engineer (Junior - Mid Level) l Onsite 66 Tower l (1 Year Contract)

DATA ENGINEER (1 Year Contract)

Junior – Mid Level | Data Platform | Lakehouse | Analytics Engineering

We are an AI-focused tech provider under Muangthai Group Holding, with a mindset of a technology start-up. We are looking to bring many proven best-in-class AI-powered products and solutions to various businesses. If you are self-motivated, want to be part of the new trend of technology and make a real impact, join us to be part of the growth story.

Location: Bangkok (Onsite 66 Tower)

Employment type: Permanent

Job Description

We are looking for a Junior to Mid-Level Data Engineer to help build and maintain scalable next-generation data intelligence platforms that support AI agent utilization of data as well as traditional analytics, reporting, and business decision-making. You will develop reliable data pipelines, help build data lake, lakehouse, and warehouse components, work with both structured and unstructured data, and build well-structured data models. You will work closely with senior data engineers, data scientists, analysts, and business teams to make data accurate, accessible, and production-ready, with strong opportunities to learn and grow toward advanced data platform work.

Data Platform & Pipelines

● Help build and maintain data platforms, including data lakes, lakehouses, and data warehouses for structured, semi-structured, and unstructured data.

● Develop data ingestion, transformation, and delivery pipelines for batch and, where applicable, near-real-time use cases.

● Develop pipelines for structured, semi-structured, and unstructured data sources, including tabular data, JSON, logs, documents, and text-based data.

● Build reliable data pipelines that support analytics, dashboards, machine learning, and downstream applications.

● Help ensure data platforms are scalable, cost-efficient, and maintainable.

Data Modeling & Data Layer Design

● Build and maintain data models that support business reporting, analytics, and machine learning use cases.

● Build and maintain well-structured data layers, such as bronze/silver/gold or raw/staging/mart layers.

● Apply data modeling best practices, including dimensional modeling and fact/dimension tables, under guidance.

● Follow data contracts, naming conventions, and documentation standards.

● Work with analytics and business teams to turn requirements into reliable data models.

Data Pipeline Development

● Develop data pipelines using Python and SQL.

● Build transformation workflows using tools such as dbt or SQLMesh.

● Build and support orchestration workflows using Dagster, Airflow, or similar tools.

● Implement data quality checks, validation rules, and monitoring, and help with failure recovery.

● Help optimize pipeline performance, cost, and reliability.

Cloud, Collaboration & Support

● Build cloud-ready data solutions; experience with AWS cloud services (S3, Glue, Athena, Lambda, RDS, IAM) is a plus, but not required.

● Support data scientists, analysts, business users, and engineering teams with data availability, data quality, and pipeline reliability.

● Troubleshoot data issues, pipeline failures, and data quality problems.

● Contribute to data engineering standards, reusable components, and internal documentation.

Qualification

Required Qualifications

● Bachelor's degree in Computer Science, Computer Engineering, Data Engineering, Information Systems, Statistics, or a related field.

● 2–4 years of hands-on experience as a Data Engineer, Analytics Engineer, or similar role (strong new graduates or those with relevant internships may be considered for the junior level).

● Proficiency in Python for data pipeline development, automation, and data processing.

● Proficiency in SQL, including joins, aggregations, window functions, and data transformation logic.

● Understanding of data pipeline concepts, ETL/ELT, and data modeling.

● Exposure to or interest in data lakes, lakehouses, or data warehouses.

● Understanding of data quality, data validation, and basic monitoring/logging.

● Ability to work with cross-functional teams and strong problem-solving skills.

● Willingness to learn new tools, technologies, and business domains.

Preferred Qualifications

● Experience with modern data transformation frameworks such as dbt or SQLMesh.

● Experience with workflow orchestration tools such as Dagster, Airflow, or similar.

● Experience with cloud services such as AWS S3, Glue, Athena, Lambda, RDS, and IAM.

● Experience with lakehouse table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.

● Familiarity with version control using Git and basic CI/CD practices.

● Interest in building data solutions for machine learning, feature engineering, or AI use cases.

● Exposure to vector databases (e.g., Qdrant) or knowledge graph and ontology concepts.

Soft Skills

● Eager to learn and open to feedback.

● Able to communicate clearly and explain technical concepts at a basic level.

● Strong collaboration mindset and willingness to support multiple teams.

● Attention to detail, especially around data quality, reliability, and maintainability.

● Comfortable working in a fast-moving environment with changing priorities.