Total Experience: 4-10 years
- Notice Period: Immediate to 30 Days
- Must have Skills: Minimum 3 years in AWS or GCP with Pyspark
3-5 years of experience in developing data pipelines using one of the data eco
system such as: Big Data (Spark)/Any cloud Big Data solutions in AWS
Education: Min. bachelor's in computer science or engineering, Information Systems,
or related fields Expectations in the job:
Collaborate with other teams to design, develop and deploy data tools that support
both operations and product use cases.
Work with the team in solving problems in big data technologies and prototype
solutions to improve our data processing architecture Responsibilities.
Works closely with the business's Data and Analytics teams and gathering technical
requirements.
Experience in building and maintaining reliable and scalable ETL pipeline on big data
& / or Cloud platform through the collection, storage, processing, and transformation
of large datasets.
Support production issues as relate to application functionality and integrations.
Must Haves: Proven experience in developing Big Data eco system/data warehouses
and ETL pipelines (Min. 3 years)
Proficient in scripting capability for analysis and reporting using any one of the
languages Pyspark
Proficient SQL development skills with ability to write complex efficient queries for
data integration.
Analytical skills to support Business Analysts and ability to translate user stories /
work closely with the tech team.
Must have strong experience in data warehouse concepts.
Experience in Database design/data modelling in Data Lake environment is highly
preferred.
Strong problem-solving skills (Math skills required for data modelling)
Ability to manage and complete multiple tasks within tight deadlines.
Working experience with any one of the cloud platforms namely AWS
Job Types: Full-time, Permanent
Pay: From 400,
- 00 per year
Jadwal: - Day shift
Experience:
- Data Engineer: 3 years (Preferred)
* AWS/GCP: 3 years (Preferred)
- Pyspark: 3 years (Preferred)
Work Location: In person
