Senior Data Engineer
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Summary
Senior Data Engineer on a 1-year extendable contract based in Bangi, Malaysia, building end-to-end Enterprise Data Lake pipelines (Bronze/Silver/Gold) including data mapping, modeling, ETL/ELT development, and data quality frameworks. Core stack: Nifi/Airflow, Python/Powershell/YAML/bash scripting, Kafka streaming, Linux/ELK, and Apache Iceberg for SCD modeling.
Duration: 1-year extendable contract
Location: Bangi
Role Summary
- Responsible for data mapping, designing, development, testing, implementing for Enterprise Data Lake Data Pipelines (Bronze, Silver, and Gold) data delivery within timeline.
Data Pipeline Development
- Documentation required: on Data Mapping, Technical Specification, and SIT plan & test cases and technical specification
- Model Design: Designing schemas for warehouses and analytical model concept for implementation.
- Development of Automated Workflows: Design, build, and maintain ETL/ELT pipelines (end-to-end) from sourcing to final data product delivery for structured and unstructured data
- Data Preparation: Cleaning and preprocessing data to make it suitable for analysis, implementing data transformation processes to extract data for the creation and loading of Data Products. Provide strategic recommendations that enhance productivity and business performance
- Data Integration: Integrate data from multiple sources into centralized platforms for analytics and reporting. API data integration to EDL pipelines with Internal and External applications. Kafka included for real time streaming.
- Data Quality: Building frameworks/program for validation, monitoring, and error handling
Required Core Skills
- Batch Scheduling: Nifi, Airflows, etc.
- Data Model Implementation: Apache Iceburg framework for SCD data model
- Scripting & Automation: Proficiency in Python, Powershell, YAML, bash, and etc.
- OS Knowledge: Strong proficiency in Linux systems and networking for integration and ELK stack management.
- Big Data & Kafka: Familiar with data streaming technologies such as Kafka
- Preferred Qualifications & Experience:
- Minimum 7 years of relevant experience in Data Engineering fields and good knowledge of Data Warehousing/Data Lake modeling and implementation.
- Strong Analytical and Problem solving skill.
- Bachelor's/ Master Degree: In Engineering, Computer Science, IT, or related technical field
- Data Engineering Certification
- Familiarity with data modeling and ETL concepts.
Added Advantages
- Knowledge of cloud platforms (AWS, Azure, or GCP) and containerization (Docker/Kubernetes).
- Knowledge of other Databases such as MongoDB, HBase, Postgress