Why this grade
This listing scored 54/100, which is a D. It lost the most ground on pay transparency.
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Freshness 15 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
- Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Data Science Developer Mid level Full Time
L3 Big datadeveloper ( 7-10 Years)
1. Design, develop, and implement highly scalable and distributed big datasolutions using Hadoop ecosystem technologies such as HBase, Hive, Kudu, andSpark.
2. Architect HBase schemas and data models to accommodate evolving businessrequirements and ensure optimal performance for data storage and retrievaloperations.
3. Develop complex Hive queries and data processing pipelines to transform rawdata into structured formats suitable for analysis and reporting.
4. Implement data ingestion pipelines using Spark Streaming and Spark SQL forreal-time processing of streaming data sources, ensuring high throughput andlow latency.
5. Optimize Spark applications for performance and resource utilization,including tuning RDD transformations, optimizing data partitioning strategies,and leveraging in-memory caching.
6. Utilize advanced features of Spark MLlib for machine learning tasks such asclassification, regression, clustering, and collaborative filtering.
7. Design and deploy Kudu tables for fast analytical queries and real-timeanalytics, leveraging Kudus unique combination of fast analytics and fast dataingestion.
8. Collaborate with data scientists to integrate machine learning models intoSpark workflows and productionize them for real-time predictions and analytics.
9. Troubleshoot performance bottlenecks, data quality issues, and systemfailures in big data applications and infrastructure, and implement solutionsto address them.
10. Stay abreast of emerging technologies and best practices in big data processingand analytics, and evaluate their potential impact on our architecture andsolutions.
Originally posted on Himalayas
Apply for this role Opens himalayas.app — the link as listed; we have not yet verified it is the employer's own page
Where this listing came from
- 01 Oct 2026 Himalayas first sighting
Seen on 1 board over 0 days.