Data Engineer (SQL, Databases, Oracle)
Just Analytics
Fl 8-Unit 802, Tòa nhà Beautiful Saigon, 2 Nguyễn Khắc Viện, Phường Tân Phú, District 7, Ho Chi Minh
Không xác định
2019-10-23 -> 2019-10-24
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Strong analytic skills related to working with unstructured datasets.
- Experience building and optimizing ‘big data’ data pipelines, architectures, and data sets.
- We are looking for a candidate with > 1+ years of experience in a Data Engineer role. They should also have experience using the following software/tools:
- Experience with big data tools: Hadoop, Spark, Kafka, etc.
- Experience with relational SQL databases such as Oracle, SQL Server, MySQL or Postgres.
- Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
- Experience with stream-processing systems: Storm, Spark-Streaming, etc.
- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
- For senior data engineers also should have demonstrable experience in
- Project management and organizational skills.
- Experience supporting and working with cross-functional teams in a dynamic environment.
- Good Academic track record preferred
- Some experience in visualization tools such as PowerBI/Tableau or other BI tools will be beneficial.
- QUALIFICATIONS
- Degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field
- 1+ years of relevant experience.
- Negotiable salary.
- Collaborate with customer to gather requirements and to understand their business processes.
- Create and maintain optimal data pipeline architecture,
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Spark, SQL, and Azure ‘big data’ technologies.
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
- Use tools to deploy and monitor the performance of the systems in production.