Data Engineer

Emagine - Portugal; Teletrabalho; BRL 322.000 - 484.000; Tempo integral

Resumo da oferta

A leading tech company is seeking a Data / ETL Engineer with strong AWS experience to build and operate data pipelines. The ideal candidate will have solid SQL skills, Python development expertise, and experience with Amazon Athena and AWS Glue. Familiarity with data lake architectures and streaming data pipelines is a plus. This role offers the opportunity to work remotely while collaborating with teams across different time zones.

Qualificações

• Solid understanding of relational databases and strong SQL skills.
• Strong Python development skills for data processing and ETL workloads.
• Hands-on experience with Amazon Athena for querying data.

Conhecimentos

SQL

Python

AWS Glue

Amazon Athena

Apache Iceberg

Ferramentas

Apache Flink

Kafka

Descrição da oferta de emprego

Profile

Data / ETL Engineer (AWS) We are looking for a Data / ETL Engineers with strong experience building and operating data pipelines in AWS.

Core skills & experience

• Solid understanding of relational databases, with strong SQL skills (experience with Microsoft SQL Server preferred)
• Strong Python development skills, particularly for data processing and ETL workloads
• Hands‑on experience with Amazon Athena for querying data in data lakes
• Experience with AWS Glue, including Glue Data Catalog, databases, and crawlers
• Knowledge of Apache Iceberg for table formats and data lake management
• Broad AWS knowledge, including data services, IAM, networking basics, and operational best practices

Nice-to-have / preferred experience

• Experience building streaming data pipelines, preferably using Apache Flink
• Familiarity with Kafka (managed or self-hosted) for event‑driven architectures
• Experience with non-relational datastores and / or column‑oriented data stores (e.g. NoSQL, data warehouse or analytics-focused systems)
• Comfortable working with modern data lake architectures in AWS
• Experience collaborating with engineers, analysts, and stakeholders around data models and ingestion patterns

To work in the Denmark time zone