Position: Senior Data Engineer (Communication Management Solution)
Company: Sigma Software Group
Experience required: 5y
English level: upper

Do you want to be a part of a talented team caring about their products and striving to make them the best on the market? If so, keep reading!   

Sigma Software is looking for a passionate Senior Data Engineer. We specialize in Cloud Technologies and love to make great products that improve our clients’ and partners’ Customer Communication and Engagement capabilities.  

Feeling thrilled? You are welcome to join!  

CUSTOMER

Our client delivers a SaaS solution for Customer Communication Management (CCM) for Telecommunications, Financial, and Energy sectors.  

PROJECT

This solution is a cloud-based multi-tenant platform that enables end-to-end capabilities to design, produce, store, and access very high volumes of documents (invoices, bills, promotions, etc.).

REQUIREMENTS

At least 5 years of experience with AWS tools such as Glue, Athena, Redshift, S3, Lambda, CloudFormation  
5+ years experience with Python  
Practical experience with Kafka  
Experience with Spark (PySpark) 
Good knowledge of SQL  
Familiarity with running and optimizing AWS Redshift DB  
Deep understanding of database development and data modeling  
Good knowledge of data engineering, e.g., dimensional modeling, ETL, reporting tools, data governance, data warehousing based on AWS infrastructure  
Experience with BI visualization tools (Tableau is the preferred one)  
Upper-Intermediate level of spoken and written English  
 

NICE TO HAVE:

Knowledge of Machine Learning and Data Science fields (Unsupervised and Supervised Learning, outlier detection)  
Experience in developing and deploying machine learning models 

RESPONSIBILITIES

Providing technical leadership to the team of data engineers implementing architectures based on AWS Cloud platforms (Glue, Lambda, S3, Redshift, etc.) 
Building Warehouse based on Redshift, Redshift Spectrum, and S3 services 
Designing, developing, deploying, optimizing, and maintaining data architecture and pipelines that adhere to defined ETL and data lake principles  
Discovering, understanding, and organizing disparate data sources and structuring them into clean data models with clear and understandable schemas 
Proposing solutions for real-time and batch data processing 
Interacting with developers and business stakeholders and the problems needing to be solved using Data Engineering methods