Senior Data Engineer (Onsite 66 Tower, Bang Na Nuea)

SENIOR DATA ENGINEER

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 Senior Data Engineer to design, build, and maintain scalable next-generation data intelligence platforms that support AI agent utilization of data as well as traditional analytics, reporting and decision making for business. You will develop reliable data pipelines, design data lake, lakehouse, and warehouse architectures, work with both structured and unstructured data, build well-structured data models, and support multiple teams in using data effectively. You will work closely with data scientists, analysts, software engineers, business teams, and platform teams to ensure data is accurate, accessible, well-modeled, and production-ready.

Data Platform & Architecture

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

● Define data architecture patterns for raw, staging, intermediate, curated, and serving layers.

● Design data ingestion, transformation, and delivery pipelines for batch and 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.

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

● Ensure data platforms are scalable, cost-efficient, secure, and maintainable.

Data Modeling & Data Layer Design

● Design and implement 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, fact and dimension tables, slowly changing dimensions, and reusable data marts.

● Define clear data contracts, naming conventions, documentation standards, and data ownership practices.

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

Data Pipeline Development

● Develop production-grade data pipelines using Python and SQL.

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

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

● Implement data quality checks, validation rules, monitoring, alerting, and failure recovery mechanisms.

● Optimize pipeline performance, cost, and reliability.

Cloud & Infrastructure

● Build and maintain cloud-ready data solutions; experience with AWS cloud services is a plus, but not required.

● Work with AWS data services such as S3, Glue, Athena, Lambda, Step Functions, RDS, and IAM when applicable.

● Design secure and efficient access patterns for data storage, processing, and consumption.

● Collaborate with infrastructure or DevOps teams to deploy, monitor, and operate data pipelines in cloud environments.

● Follow cloud security, governance, and cost optimization best practices.

Cross-Team Support & Collaboration

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

● Collaborate with stakeholders to understand data requirements and turn them into scalable technical solutions.

● Provide technical guidance to team members on data engineering best practices.

● Review data pipeline designs, SQL logic, data models, and production readiness.

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

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

Qualification

Required Qualifications

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

● At least 4 years of hands-on experience as a Data Engineer, Analytics Engineer, Data Platform Engineer, or similar role.

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

● Strong proficiency in SQL, including complex queries, joins, window functions, CTEs, performance tuning, and data transformation logic.

● Experience designing and implementing data lakes, data lakehouses, or data warehouses.

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

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

● Strong understanding of data modeling concepts, data layers, data marts, and analytical data design.

● Experience with cloud-based data architecture; AWS cloud experience is a plus, but not required.

● Understanding of data quality, data validation, monitoring, logging, and pipeline observability.

● Ability to work with cross-functional teams and translate business requirements into technical solutions.

● Strong problem-solving skills and ability to debug complex data issues.

Preferred Qualifications

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

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

● Experience with CI/CD practices for data pipelines and analytics code.

● Experience with version control using Git.

● Experience designing data solutions for machine learning, feature engineering, or model inference pipelines.

● Experience with vector databases such as Qdrant or similar tools for embedding storage, similarity search, or AI application use cases.

● Experience with data governance, metadata management, lineage, and access control.

● Experience mentoring junior engineers or leading technical implementation within a data team.

● Understanding of knowledge graph and ontology concept.

Soft Skills

● Strong communication skills and ability to explain technical concepts clearly.

● Ability to work independently and take ownership of data engineering solutions.

● Strong collaboration mindset and willingness to support multiple teams.

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

● Ability to balance business needs, technical quality, and delivery timelines.

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