Junior Data Engineer

Responsibilities

• End-to-End Solutions: You will design and develop end-to-end solutions for processing large data volumes, spanning from the initial business problem to scalable and optimized ETL/ELT pipelines.

• Tech Stack: You will work with technologies such as Databricks, Snowflake, dbt, and Microsoft Fabric, using Spark (Python/Scala), SQL; and orchestration with Airflow, Data Factory or similar tools.

• Advanced Architectures: You will help to define and implement advanced architectures under the Data Lakehouse and Data Warehouse paradigms, supporting diverse data processing patterns including Batch, Near Real Time, and Streaming.

• Data Integration: You will develop data ingestion and transformation pipelines from diverse sources (APIs, files, databases, streaming), to build analytical data models supporting business use cases.

• Platform Foundation: You will collaborate in the implementation of frameworks and engines that provide a standard framework for the main functions of a data platform: Orchestration, Ingestion, Transformation, Quality, Security, Testing, Deployment, Observability, among others.

• Collaboration: You will collaborate with multidisciplinary teams and stakeholders, translating complex requirements into efficient technical solutions.

• Continuous Learning: You will stay up to date with the latest technological trends—especially in Data & Analytics—through continuous training and the exploration of new tools and innovations.

• Career Path: You will be in control of your professional development together with your managers. SDG Group will give you the opportunity to build a professional career oriented to become a Data Architect in a world-class organization.

Requirements

• Education: Degree in Computer Engineering or Computer Science. It’s a plus to have a Master’s Degree in any Data-related field.

• Languages: English C1 level or higher.

• Experience: 2+ years of experience in Data Lake and/or Data Warehouse projects.

• ETL/ELT: Experience on data integration with Spark (Python or Scala) and/or SQL and dimensional data modeling.

• Cloud: Experience working in Cloud environments (AWS, GCP, or Azure) and platforms (Databricks, Snowflake, Microsoft Fabric).

• Code Management & DataOps: Solid understanding of Git, CI/CD, pipeline monitoring, data quality control, and data versioning.

• Consulting Background: Previous experience in consulting is a plus.