As an experienced Data Engineer, you will be responsible for designing, developing, and maintaining robust and scalable ETL/ELT data pipelines using Databricks, Python, and SQL.
You will integrate data from multiple sources including databases, files, and APIs.
Your role will involve transforming and cleaning data to ensure its quality and consistency.
You will automate data processing tasks for efficient and reliable execution.
Setting up monitoring and alerting processes to ensure the availability and health of the pipelines will be part of your responsibilities.
You will collaborate with data analysts and data scientists to understand their data needs and provide high-quality data.
Documenting data pipelines and processes clearly and concisely is essential.
Staying updated on the latest technologies and trends in Big Data and data analytics is expected.
Requirements:
A degree in computer engineering, mathematics, statistics, or a related field is required.
Proficiency in English, both written and spoken, is mandatory.
A minimum of 5 years of experience as a Data Engineer is required.
Expertise in Databricks, Python, and SQL is essential.
Experience in data integration, data transformation, and data cleaning is necessary.
A solid understanding of Big Data data architectures is required.
Experience in developing batch and streaming data pipelines is needed.
Experience in setting up monitoring and alerting processes is essential.
Excellent communication and collaboration skills are required.
The ability to work independently and manage multiple projects simultaneously is necessary.
A team spirit and willingness to share knowledge are important.
Benefits:
You will be part of a human-sized company with great ambitions, working alongside over 2200 talents across more than 13 countries and 3 continents.
The company culture encourages creativity and boldness.
You will have the opportunity to work remotely, providing flexibility in your work environment.
The role offers a chance to stay at the forefront of technology and trends in the field of Big Data and data analytics.
