- Singapore
IMPACT AI TECHNOLOGIES PTE. LTD. Hiring! Full Time Data Engineer in , Earn up to SGD 10,000 - Ricebowl
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Job Description
Responsibilities
About the Role
We are looking for a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms. In this role, you will independently build, optimize, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality and reconciliation across our ecosystem.
Key Responsibilities
Pipeline Engineering: Develop and maintain robust batch, streaming, CDC, and API-based data pipelines, historical snapshots, and SCD frameworks.
Data Transformation: Build transformations for EDW, data lakes, and semantic layers, including specialized payment harmonization datasets.
Performance & Quality: Tune SQL queries and Spark jobs, implementing stringent reconciliation controls, error handling, and operational monitoring.
SDLC Delivery: Support SIT, UAT, defect resolution, release execution, and production verification in close collaboration with architecture and BI teams.
What You Will Need to Succeed
6–9 years of relevant experience in Data Engineering
Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline
Preferred Certifications: Teradata, Cloudera, Spark, or Cloud Data Engineering
Technical Stack: Advanced proficiency in SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera ecosystems.
Tools & Integration: Experience with Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git-based version control.
Domain Exposure: Strong background in enterprise data warehousing, data lakes, and banking data operations (Payments, Risk, Finance, or AML preferred).
Core Competencies: Independent ownership of deliverables, analytical mindset, and a commitment to secure, high-quality engineering standards.
We are looking for a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms. In this role, you will independently build, optimize, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality and reconciliation across our ecosystem.
Key Responsibilities
Pipeline Engineering: Develop and maintain robust batch, streaming, CDC, and API-based data pipelines, historical snapshots, and SCD frameworks.
Data Transformation: Build transformations for EDW, data lakes, and semantic layers, including specialized payment harmonization datasets.
Performance & Quality: Tune SQL queries and Spark jobs, implementing stringent reconciliation controls, error handling, and operational monitoring.
SDLC Delivery: Support SIT, UAT, defect resolution, release execution, and production verification in close collaboration with architecture and BI teams.
What You Will Need to Succeed
6–9 years of relevant experience in Data Engineering
Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline
Preferred Certifications: Teradata, Cloudera, Spark, or Cloud Data Engineering
Technical Stack: Advanced proficiency in SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera ecosystems.
Tools & Integration: Experience with Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git-based version control.
Domain Exposure: Strong background in enterprise data warehousing, data lakes, and banking data operations (Payments, Risk, Finance, or AML preferred).
Core Competencies: Independent ownership of deliverables, analytical mindset, and a commitment to secure, high-quality engineering standards.
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