Senior Data Engineer Big Data & Cloud Integration L3 Hybrid - US

Senior Data Engineer Big Data & Cloud Integration L3

Full Time • Hybrid - US
Replies within 24 hours
Benefits:
  • HYBRID
  • Competitive salary
  • Opportunity for advancement

Senior Data Engineer – Big Data & Cloud Integration                                                     
DALLAS, TX - HYRBID           
LONG TERM   
IN-PERSON INTERVIEW             
Data Engineer - L3 ROLE               


- Translate complex cross-functional business requirements and functional specifications into logical program designs and data solutions. 
- Partner with the product team to understand business needs and specifications.
- Solve complex architecture, design and business problems. 
- Coordinate, Execute and participate in component integration (CIT) scenarios, system integration testing (SIT), and user acceptance testing (UAT) to identify application errors and to ensure quality software deployment. 
- Continuously work with cross-functional development teams (Data Analysts and Software Engineers) for creating PySpark jobs using Spark SQL and help them build reports on top of data pipelines. 
- Build, test and enhance data curation pipelines, integrate data from a wide variety of sources like DBMS, File systems and APIs for various OKRs and metrics development with high data quality and integrity.  
- Execute the development, maintenance, and enhancements of data ingestion solutions of varying complexity levels across various data sources like DBMS, File systems (structured and unstructured), APIs and Streaming on on-prem and cloud infrastructure. 
- Responsible for the design, implementation, and architecture of very large-scale data intelligence solutions around big data platforms. 
- Work with building data warehouse structures, and creating facts, dimensions, aggregate tables, by dimensional modeling, Star and Snowflake schemas. 
- Develop spark applications in PySpark on distributed environment to load huge number CSV files with different schema in to Hive ORC tables. 
- Perform ETL transformations on the data loaded into Spark Data Frames and do the in-memory computation. 
- Develop and implement data pipelines using AWS services such as Kinesis, S3 to process data in real-time. 
- Work with monitoring, logging and cost management tools that integrate with AWS. 
- Schedule the spark jobs using Airflow scheduler to monitor their performance.     

....

Flexible work from home options available.

Compensation: $60.00 - $65.00 per hour




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