Data Engineer SR

Somos o maior banco privado brasileiro em valor de mercado e a marca mais valiosa do Brasil, avaliada em R$ 44,3 bilhões segundo o ranking Interbrand 2023, e estamos em 1º lugar entre as 10 melhores empresas para se trabalhar no Brasil em 2023 com mais de 10 mil funcionários, segundo o ranking Great Places to Work.

Com a maior prateleira de produtos do setor no país e por meio de nossas marcas e parcerias comerciais, oferecemos um amplo leque de oportunidades de carreira em diversas áreas de especialização, tudo com um grande propósito – encantar os nossos clientes, entregando o que eles precisam na hora que eles precisam!

Hoje contamos com um time de

97,1

mil colaboradores (os itubers) espalhados pelo Brasil e exterior, em que estamos presentes em 18 países. /n

What is the day to day like for an Ituber in this area? Design, build, and continuously evolve

scalable, resilient, and high‑performance data pipelines , supporting a wide range of analytical, operational, and data science use cases. Promote the

democratization of data access , ensuring datasets are reliable, well documented, properly governed, and easily consumable by analysts, data scientists, and business teams. Develop

ETL/ELT pipelines

using

Python, SQL, and Spark/PySpark , processing large volumes of data in distributed environments. Implement and maintain

data solutions on AWS , leveraging services such as

S3, Glue, EMR, Lambda, Redshift, Athena, Kinesis, and MWAA (Airflow) , with a strong focus on efficiency, cost optimization, and scalability. Define and apply

analytical data models

(Data Warehouse, Data Lakehouse), ensuring adherence to best practices for performance, usability, and data consumption.

Required Technical Skills Advanced / Fluent English – Mandatory Strong proficiency in

Python

for building data pipelines, automation, and data processing workflows. Advanced knowledge of

SQL , including complex queries, window functions, CTEs, and performance optimization. Hands-on experience with

Apache Spark / PySpark

for large-scale, distributed data processing. Solid experience designing and implementing

ETL/ELT pipelines

in cloud-based and distributed environments. Proven experience with

AWS

data services, including: Storage & Data Lake:

Amazon S3, AWS Lake Formation Processing:

AWS Glue, EMR, Lambda Analytics & Warehousing:

Amazon Redshift, Athena Streaming:

Kinesis Data Streams / Firehose Orchestration:

MWAA (Managed Airflow), Step Functions Strong understanding of

analytical data modeling , including

Data Warehouse (Star/Snowflake schemas)

and

Data Lakehouse

architectures. /n

What do you need to become an Ituber? We are looking for people who want to continuously grow and learn, sharing, collaborating, innovating, and delivering value to all of our customers.Here, we don’t know everything — we move forward together as a team.