该职位来源于猎聘 Main Responsibilities
- Build end-to-end data pipelines including data collection, transformation, quality and integration solutions to facilitate CLP's data & analytics solutions.
- Collaborate with solution design and business requirements teams to identify data requirements and assemble large, complex data sets that meet the requirements
- Design, implement and fine-tune analytics solutions that meet business and technical requirements
- Work collaboratively with the CLP data architect to ensure data model integrity
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
- Create data tools for analytics and data scientists teams that assist them in building and optimising the data models and solutions
- Work with the DevOps engineer to support the consistent operation of data & analytics solutions
- Work closely with the data analysts & data scientists to design and develop APIs Qualifications Academic Qualification:
- Bachelor or Masters degree in a related field (e.g. computer science, information technology, etc.) Professional Experience:
- At least 3 years experience in SQL/PostgreSQL, data and BI solutions with integration to 3rd party tools
- Experience working with application server software (e.g. ERP), Spark, Scala, Python, SQL scripting languages, relational databases (e.g. SQL DB/DW), NoSQL platforms (e.g. HBase, MongoDB, Cassandra), cloud technologies (e.g. Azure)
- Experience with Data Lake, Databricks, Data Factory, BI Dashboard, and BI implementation projects
- Highly experienced with processing large and complex datasets and building end-to-end data pipelines using on-premise or cloud-based data platforms
- Experienced in coding in data management, data warehousing or unstructured data environments
- Experience in the energy sector or other asset-intensive industries will be highly regarded
- Experience in using coding assistants (e.g. GitHub Copilot) for uplifting productivity in development
- Familiar with GenAI concepts like RAG, orchestration frameworks like LangChain and LlamaIndex, vector databases etc. to collaborate efficiently with data scientists and ML engineers in AI use cases
Competencies Technical (Functional):
- Experience in Azure cloud platforms and technology
- Ability to define and develop data integration patterns and pipelines
- Strong knowledge of data modelling, data warehousing, and BI concepts
- Self-motivated, able to work independently, and attention to details
- Curious about emerging AI trends and their implications for data engineering