LeverX

Middle Data Engineer

თბილისი
სექტემბერი 26, 2024
Application ends: ოქტომბერი 26, 2024
CV გაგზავნა

CV გაგზავნა

ატვირთეთ CV (doc, docx, pdf)
ვაკანსია დაიხურება:
ოქტომბერი 26, 2024

ვაკანსიის დეტალები

LeverX is looking for Middle Data Engineer. Don’t miss this exciting opportunity to gain experience working with a global IT partner.

REQUIREMENTS

  • Skilled in crafting and fine-tuning complex SQL queries and managing relational databases.
  • Proficient in Python for data manipulation and automation, with experience using libraries like Pandas and NumPy.
  • Cloud platform knowledge, preferably AWS, AWS Certifications would be a huge benefit.
  • Understanding Git CI/CD.
  • Experienced in DWH design and architecture, including schema development, data modeling, and performance optimization
  • Knowledgeable about cloud-based data warehousing platforms (e.g., AWS Redshift, Google BigQuery, Snowflake).
  • Strong analytical and problem-solving capabilities.
  • Effective communicator with the ability to work collaboratively across teams.
  • Proficiency in English at a B2 level.

RESPONSIBILITIES

  • Data Warehouse Design: Develop and implement data warehouse structures that are scalable, efficient, and aligned with business needs.
  • Data Pipeline Creation: Architect, build, and oversee scalable ETL workflows to gather, transform, and store extensive data from varied sources.
  • Database Oversight: Enhance and manage SQL databases to ensure optimal data storage, retrieval, and system performance.
  • Data Integration: Consolidate data from different sources, ensuring its quality, consistency, and accessibility.
  • Automation and Scripting: Utilize Python and PySpark to streamline and automate data processing tasks and workflows.
  • Team Collaboration: Engage with stakeholders to grasp their data requirements and deliver effective data solutions.
  • Performance Enhancement: Evaluate and refine data workflows and queries to achieve peak performance and efficiency.
  • Process Documentation: Create and keep detailed records of data engineering practices, system architectures, and workflows.
  • Issue Resolution: Address and resolve data-related issues to maintain data accuracy and reliability.