Resumen del puesto por JobGrid
L1 Data Engineer - Remote at DeepSource Technologies: Remoto, Ucrania; Jornada completa; Junior; Tecnología; Ingeniería de datos. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Remoto, Ucrania
- Role classification: Tecnología, Ingeniería de datos, Jornada completa, Junior
- Source freshness: checked by JobGrid on 2026-05-31.
- Application path: candidates continue to the employer application page with non-personal referral tags.
We are looking for a motivated and technically solid L1 Data Engineer to join our growing Data & Analytics team. In this role, you will be responsible for designing, building, and maintaining the data architecture and infrastructure that supports our organization's data strategy. You will work hands-on to develop, test, and deploy reliable data solutions — ensuring pipelines are scalable, efficient, and aligned with business requirements.
This is an ideal opportunity for a data professional who is eager to deepen their expertise in cloud-native data platforms, particularly within the Microsoft Azure and Databricks ecosystem, and who thrives in a collaborative, fast-paced environment.
KEY RESPONSIBILITIES
• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support business intelligence and analytics use cases.
• Build and optimize data ingestion processes using Azure Data Factory and Databricks, ensuring data quality and consistency across all layers of the data platform.
• Transform and process large datasets using PySpark and Python, applying best practices for performance and maintainability.
• Write and optimize complex SQL queries to support analytical reporting and data validation requirements.
• Collaborate with data architects and senior engineers to implement and maintain data models aligned with organizational standards.
• Monitor, troubleshoot, and resolve pipeline failures and data quality issues, applying root-cause analysis to prevent recurrence.
• Contribute to documentation of data pipelines, data dictionaries, and engineering standards.
• Support the team in exploring and evaluating new tools and approaches to continuously improve the data infrastructure.