Data Engineer

IMPACT AI TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

IMPACT AI TECHNOLOGIES PTE. LTD. is seeking a hands-on Data Engineer to drive scalable ETL pipelines and secure data platforms in our enterprise environment.

You will build, optimize, and guard data queries, datasets, and architectures across the stack. Ideal candidates bring 6–9 years of data engineering experience, a CS/engineering degree, and hands-on mastery of SQL, Python, Spark/PySpark, and big data ecosystems.

Qualifications

• 6–9 years of relevant Data Engineering experience.
• Bachelor’s degree in Computer Science, Engineering, Information Technology, or related discipline.
• Preferred Certifications: Teradata, Cloudera, Spark, or Cloud Data Engineering.
• Technical Stack: SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera ecosystems.
• Tools & Integration: Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git 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 high-quality engineering standards.

Responsibilities

• Pipeline Engineering: Develop and maintain 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 payment harmonization datasets.
• Performance & Quality: Tune SQL queries and Spark jobs, implement reconciliation controls, error handling, and monitoring.
• SDLC Delivery: Support SIT, UAT, defect resolution, release execution, and production verification with architecture and BI teams.

Skills

Data engineering

SQL

Python

Spark

Analytics mindset

Independent ownership

Education

Bachelor’s degree in CS/Engineering/IT

Tools

Teradata

Cloudera

Spark/PySpark

Hadoop ecosystem

Informatica/Talend

Hive/Impala

Kafka

Airflow

Git

Job description

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.