Requirements

Expected technologies

Python

AWS

dbt

ETL

GitLab

Bash

Docker

Jira

SQL

Our requirements

• Minimum 5 years of experience in data engineering.
• Strong experience with SQL and AWS Big Data technologies.
• Hands-on experience with AWS services: S3, Step Functions, Lambda, Fargate, AWS Glue, RDS.
• Proficiency in building and managing ETL pipelines using AWS.
• Expertise in dbt (data build tool) for data transformation.
• Solid experience with CI/CD pipelines, particularly with GitLab and DataOps.
• Strong knowledge of Bash, Docker, and Python for process automation and simplification.
• Familiarity with JIRA for project management.

Your responsibilities

• Build and maintain optimal data pipeline architecture.
• Assemble large, complex datasets that meet both functional and non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual tasks, optimizing data delivery, and redesigning infrastructure for greater scalability.
• Build infrastructure for the optimal extraction, transformation, and loading (ETL) of data from a variety of sources using SQL and AWS Big Data technologies.
• Develop CI/CD pipelines (GitLab, DataOps).
• Model data transformations using dbt (data build tool), including scripting and complex algorithms.
• Automate and simplify processes using Bash, Docker, Python, GitLab, and JIRA.
• Use AWS services (S3, Step Functions, Lambda, Fargate, AWS Glue, RDS) to handle ETL processes.
• Build analytics tools for actionable insights into customer acquisition, operational efficiency, and business performance.
• Work with stakeholders, including Executive, Product, Data, and Design teams, to resolve data-related issues and support infrastructure needs.
• Ensure data security across national boundaries and AWS regions.
• Collaborate with data and analytics experts to optimize data systems for greater functionality