Responsibilities:
- Create, construct, and manage pipelines and batch or real-time data systems.
- Create extract, transform, and load (ETL) procedures that assist in the extraction and manipulation of data from various sources.
- Uphold and improve the data infrastructure needed for precise data loading, transformation, and extraction from a range of data sources.
- Data ingestion, aggregation, and ETL processing workflows can be automated.
- Raw data from OLAP databases can be transformed into a dataset that is usable by technical and non-technical stakeholders.
- Data scientists and functional leaders from various business units can collaborate to implement machine learning models.
- Utilize data controls to maintain data privacy, security, compliance, and quality for assigned areas of ownership;
- Monitor data systems performance and apply optimization strategies;
- Use quality control methods to ensure data accuracy, integrity, privacy, security, and compliance;


Qualification:
- More than two years of relevant work experience
- Expertise in relational databases, database architecture, and advanced SQL
- Outstanding command of data pipeline and workflow management
- Proficiency in developing and implementing machine learning models Strong mathematical, analytical, and problem-solving abilities; familiarity with the Kubernetes container platform outstanding organising and communication abilities
- Demonstrated ability to work both independently and collaboratively
- Prior experience with systems linked to data streaming (e.g., Apache Kafka, MySQL, Apache Pino, etc.)
- Previous experience with distributed computing and big data sets (e.g., Hadoop/Spark, Presto/Trino, Superset, etc.)
- Expertise in programming languages, such as Go, Java, Python, etc.

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