Engenheiro de Dados GCP (Analytics) Pleno

Lever, Inc.; Brasil; Teletrabalho; BRL 180 000 - 300 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

100% remote work

Resumo da oferta

Lever, Inc. partners with a client in Brazil to hire an Engenheiro de Dados GCP (Analytics) Pleno based in Brazil. This role centers on building scalable data solutions in cloud environments with a focus on Google Cloud Platform.

You will develop ETL/ELT pipelines, work with Databricks, Spark, Python, and SQL, and contribute to Data Lake, Data Warehouse, Lakehouse architectures while collaborating with BI, Analytics, and Data Science teams.

Qualificações

• 4+ years of professional experience as a Data Engineer.
• Hands-on with GCP services (BigQuery, Cloud Storage, Dataflow).
• Experience with Databricks and Spark/PySpark.
• Proficient in Python and SQL for data processing.
• Experience with data modeling and governance.
• Exposure to Airflow and Git in collaborative workflows.
• Knowledge of LGPD and BI tools is a plus.

Responsabilidades

• Develop and maintain scalable ETL/ELT data pipelines.
• Build ingestion, transformation, and availability solutions on GCP.
• Design distributed processing with Databricks and Spark/PySpark.
• Evolve Data Lake, Data Warehouse, Lakehouse architectures.
• Integrate data from APIs, databases, and streaming platforms.
• Maintain data quality, governance, reliability across pipelines.
• Monitor and optimize cloud performance and costs.
• Collaborate with BI/Analytics/Data Science teams.

Conhecimentos

Analytical thinking

English proficiency

Problem-solving

Communication skills

Ferramentas

Databricks

Spark

PySpark

Python

SQL

BigQuery

Cloud Storage

Dataflow

Airflow

Kafka

Pub/Sub

Terraform

Docker

Delta Lake

Git

Descrição da oferta de emprego

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados GCP (Analytics) Pleno based in Brazil.

This role focuses on building and maintaining scalable data solutions in cloud environments, with a strong emphasis on Google Cloud Platform.
You will develop reliable data pipelines that enable high-quality information to support analytics and strategic decision-making.
The position combines data engineering, distributed processing, architecture, and cloud optimization in a technology-driven environment.
You will work across data ingestion, transformation, modeling, governance, and availability while ensuring performance and reliability.
The role provides opportunities to work with technologies such as GCP, Databricks, Spark, Python, SQL, and modern data architectures.
You will collaborate closely with BI, Analytics, and Data Science teams to turn diverse data sources into valuable and accessible information.
It is an opportunity to contribute to scalable Data Lake, Data Warehouse, and Lakehouse solutions while continuously improving data quality and cloud efficiency.

Accountabilities:

• Develop and maintain scalable and efficient ETL/ELT data pipelines, ensuring that data flows reliably through ingestion, transformation, and delivery processes.
• Build data ingestion, transformation, and availability solutions within Google Cloud Platform environments, using appropriate cloud services to support analytics requirements.
• Design and implement distributed data processing solutions using Databricks and Spark/PySpark, focusing on performance, scalability, and maintainability.
• Design, evolve, and support modern data architectures, including Data Lake, Data Warehouse, and Lakehouse environments.
• Integrate data from multiple sources, including APIs, databases, and streaming platforms, ensuring consistent and reliable access to information.
• Establish and maintain practices that support data quality, governance, reliability, and consistency across pipelines and storage environments.
• Monitor cloud performance and optimize infrastructure and data workloads to improve efficiency and manage costs effectively.
• Collaborate with BI, Analytics, and Data Science teams to understand data needs and deliver reliable solutions that support analytical and business objectives.

Requirements:

• At least 4 years of professional experience as a Data Engineer, with a solid track record of developing and maintaining data solutions.
• Practical experience with Google Cloud Platform, particularly services such as BigQuery, Cloud Storage, Dataflow, or similar technologies.
• Hands-on experience with Databricks and distributed data processing using Spark, including practical knowledge of PySpark.
• Strong proficiency in Python and SQL, with the ability to develop data processing solutions and query and manipulate complex datasets.
• Experience with relational and dimensional data modeling, including the ability to structure data appropriately for analytical use cases.
• Experience with pipeline orchestration tools such as Airflow or similar technologies.
• Familiarity with Git and code versioning practices used in collaborative software and data engineering environments.
• Experience with Delta Lake, Terraform, Docker, or data streaming technologies such as Kafka and Pub/Sub is desirable.
• GCP or Databricks certifications are considered a plus, as is experience designing Lakehouse architectures.
• Knowledge of BI and data visualization tools such as Power BI, Looker, or Tableau is a differentiator.
• Familiarity with data governance and LGPD requirements is valued, along with intermediate or advanced English proficiency.
• Strong analytical and problem-solving skills, with a focus on data quality, performance, scalability, and continuous improvement.

Benefits:

• 100% remote work.

We appreciate your interest and wish you the best!

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