Data Quality Analyst
3 days ago
**Let’s talk about the role and responsibilities**:
- Build robust, efficient and reliable data pipelines consisting of diverse data sources to ingest and process data
- Design and develop real time streaming and batch processing pipeline solutions
- Assemble large, complex data sets to meet functional / non-functional requirements
- Design, develop and implement data pipelines for data migration and collection, data analytics and other data movement solutions
- Work with stakeholders and data analyst teams to assist with data-related technical issues and support their data infrastructure needs
- Collaborate with Architects to define the architecture and technology selection
**Let’s talk about your qualifications and experience**:
- A minimum of 8-12 years previous experience as a Data Engineer with at least 2-3+ years of AWS hands-on technical experience
- Previous experience in programming eg. Core Java/Python/Scala programming; Hadoop Stack eg. Hive, Spark (Core and Streaming), Kafka, Flink, NIFI;
- Experience working on stream transform using Spark
- Experience working on change data capture in a big data ecosystem;
- Experience in building real time or batch ingestion and transformation pipelines;
- Should be able to work on AWS - S3, EMR, Redshift
- Should be able to implement the Data Vault & start schema Framework using redshift
- Should be able to work with Devops team on Orchestrating Airflow works
- Good understanding on iPython and Bigdata and Hands on SQL
Pay: RM10,000.00 - RM15,000.00 per month
**Education**:
- Bachelor's (preferred)
**Experience**:
- AWS - S3, EMR, Redshift: 2 years (preferred)
- Data Vault & start schema Framework: 2 years (preferred)
- Orchestrating Airflow works: 1 year (preferred)
- iPython and Bigdata: 1 year (preferred)
- Hands on SQL: 1 year (preferred)
- Data Engineer: 5 years (required)
- AWS hands-on: 2 years (preferred)
- Core Java/Python/Scala programming;: 2 years (preferred)
- Hadoop Stack: 2 years (preferred)
- Hive, Spark (Core and Streaming), Kafka, Flink, NIFI;: 2 years (preferred)
- stream transform using Spark: 2 years (preferred)
- data capture in a big data ecosystem: 2 years (required)
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