Data Engineer/Data Testing
Summary
A hybrid data engineering and QA role in Singapore: the person designs, builds, and maintains scalable ETL/ELT pipelines and data warehouses, and also designs and runs data validation, reconciliation, and automated quality tests. Core stack includes SQL, Python, ETL tools (Airflow, dbt, Informatica, Talend), cloud platforms (AWS/Azure/GCP), and testing frameworks like Great Expectations and PyTest
Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and load data from multiple sources
Develop and optimize data models, warehouses, and data lakes
Collaborate with data analysts, scientists, and business stakeholders to understand data requirements
Ensure efficient data storage, retrieval, and processing performance
Monitor pipeline performance and troubleshoot production issues
Implement data governance, security, and compliance best practices
Data Testing & QADesign and execute test plans and test cases for data pipelines, transformations, and reports
Perform data validation, reconciliation, and integrity checks across source and target systems
Develop automated data quality checks and testing frameworks
Identify, document, and track data defects, working with engineering teams on resolution
Conduct regression testing for data pipeline changes and migrations
Validate business logic and transformation rules against requirements
Create and maintain test documentation, including test strategies and QA sign-off criteria
Required Skills & QualificationsBachelor's degree in Computer Science, Information Technology, or related field
3+ years of experience in data engineering, data testing, or QA (adjust based on seniority)
Strong proficiency in SQL and at least one programming language (Python preferred)
Experience with ETL/ELT tools (e.g., Informatica, Talend, dbt, Apache Airflow)
Hands-on experience with cloud data platforms (AWS, Azure, or GCP)
Familiarity with data warehousing concepts (Snowflake, Redshift, BigQuery, etc.)
Experience with data testing/automation frameworks (e.g., Great Expectations, PyTest, DBT tests)
Understanding of data modeling, data governance, and data quality principles
Strong analytical and problem-solving skills with attention to detail
Preferred QualificationsExperience with CI/CD pipelines for data workflows
Knowledge of version control tools (Git)
Exposure to Agile/Scrum methodologies
Experience with BI tools (Power BI, Tableau) for data validation purposes
Soft SkillsStrong communication skills to liaise between technical and business teams
Ability to work independently and manage multiple priorities
Detail-oriented mindset with a quality-first approach