Position: Principal Data Engineer (IRC139545)
Company: GlobalLogic
Experience required: 3y
English level: intermediate

Requirements:
• 4+ years building conceptual, logical and/or physical database designs, or the pipelines required to build them
• 2+ years of hands-on Python experience (especially WHL and packed code)
• Expert level knowledge of using SQL to write complex, highly optimized queries across large volumes of data.
• 2+ years of Kimball (Dimensional Modeling) Expertise
• 2+ years of Spark experience (especially Databricks Spark and Delta Lake)
• Experience with source control (git) on the command line
• Strong hands-on experience implementing big-data solutions in the Azure Data Lake ecosystem (Azure Data Lake, Databricks).
• Ability to work independently and provide guidance to junior data engineers.
• (preferred) Experience with big data pipelines or DAG Tools (Dbt, Data Factory, Airflow, or similar)
• (preferred) Experience with Kafka or other streaming technology (or a willingness to learn)
• (preferred) Experience with database deployment pipelines (i.e., dacpac’s or similar technology)
• (preferred) Once or more unit testing or data quality frameworks
• (bonus) Experience with MLFlow and other MLOps pipeline technology

Responsibilities:
• Take major part in requirements definition, architecture, and design;
• Architect of subsystems and understand the impact across products;
• Design and implement core data flow components in accordance with project standards;
• Regular communications with the client and teams;
 •Ensure project delivery in a timely matter;
• Lead engineering efficiency efforts. Improve operational efficiency;
 •Daily project activities (daily standups, technical advisory, reporting, etc);
• Collaborating with the Dev Leads and Product Manager to ensure that the requirements;
• Managing daily project activities.

Department/Project Description
The Client is the largest cloud computing and virtualization technology company.
Our team is working on the development of a sophisticated Analytics as a Service (AaaS) solution.
As a part of the team, you will contribute to the creation of means for data ingestion, validation/verification, multiple storage zones, processing, and analytics on top of that. The final solution can be used by teams of various maturity or size for smaller teams looking for the basics, or large teams interested in advanced APIs and tooling.