Position: Data Architect
Company: Avenga
Experience required: 5y
English level: upper

Our client’s solution is helping brands create commerce experiences their customers love in the messaging channels they already use everyday. Company with a global reach, serving more than 220 countries, which innovative mobile messaging solutions enable Chat Commerce for the world's leading brands.

**What you bring along**
6+ years of hands-on experience as a Data Engineer;
Experience with AWS Data services. Athena, Redshift, Kinesis, Kafka, Glue, S3, Lake Formation;
Knowledge of one or more database technologies (MySQL, PostgreSQL, SQL Server, Oracle);
Ability to use BI tools and technologies (Talend, Power BI, SiSence);
Confidence in using of Big Data technologies. Hadoop, Spark, Hive, MongoDB, Presto, Flume;
Experience with complicated account hierarchy, security groups, VPCs, AWS managed services, S3, ELB, EC2 Auto Scaling, Lambda;
Familiarity with Agile and Scrum methodologies;
Hands on experience leading large-scale global data warehousing and analytics projects;
Significant experience developing Enterprise Grade applications at scale;
Understanding of database and analytical technologies in the industry including MPP and NoSQL databases, Data Warehouse design, BI reporting, and Dashboard development;
English – Upper-intermediate or higher.

**Nice to have**
Data Architecture Certification;
TOGAF Certification.

**This is your mission**
Design and develop highly scalable data ingestion and processing frameworks to transform a variety of datasets, capture metadata, lineage and implement data quality using AWS cloud services;
Work closely with Data Modelers and Data Engineers to define and evolve our data model support data analytics activities, and ensure platforms scalability;
Ensure data models and architectural patterns fit Product requirements and overall product value prepositions;
Work closely with the data delivery teams to drive execution and continue improvement of Data Products;
Design, implement and document technical Data solutions using AWS Data services and solutions;
Perform problem resolution and root cause analysis along with recommending and implementing preventative techniques;
Review existing ETL architecture and frameworks to improve performance, manage costs and drive efficiencies;
Manage priorities, estimation, and Data Architecture artifacts in support of Data Product development.