Data Engineer

This role has a specialized focus on building and maintaining robust, scalable, and automated data pipelines and plays a key role in optimizing our data infrastructure and enabling efficient data delivery across the organization. As the organization enhances its cloud data platform (Snowflake or something similar), this role will be instrumental in implementing and managing CI/CD processes, infrastructure as code (Terraform), and data transformation workflows (dbt).

Job Responsibilities:

Design, build, and maintain scalable and resilient CI/CD pipelines for data applications and infrastructure, with a focus on Snowflake, dbt, and related data tools.
Implement and manage Snowflake dbt projects for data transformation, including developing dbt models, tests, and documentation, and integrating dbt into CI/CD workflows.
Develop and manage infrastructure as code (IaC) using Terraform to provision and configure cloud resources for data storage, processing, and analytics on GCP.
Automate the deployment, monitoring, and management of Snowflake data warehouse environments, ensuring optimal performance, security, and cost-effectiveness.
Collaborate with data engineers and data scientists to understand their requirements and provide robust, automated solutions for data ingestion, processing, and delivery.
Implement and manage monitoring, logging, and alerting systems for data pipelines and infrastructure to ensure high availability and proactive issue resolution.
Develop and maintain robust automation scripts and tools, primarily using Python, to streamline operational tasks, manage data pipelines, and improve efficiency; Bash scripting for system-level tasks is also required.
Ensure security best practices are implemented and maintained across the data infrastructure and pipelines.
Troubleshoot and resolve issues related to data infrastructure, pipelines, and deployments in a timely manner.
Participate in code reviews for infrastructure code, dbt models, and automation scripts.
Document system architectures, configurations, and operational procedures.
Stay current with emerging DevOps technologies, data engineering tools, and cloud best practices, particularly related to Snowflake, dbt, and Terraform.
Optimize data pipelines for performance, scalability, and cost.
Support and contribute to data governance and data quality initiatives from an operational perspective.
Help implement AI features
Bachelor's degree in Computer Science, Engineering, or a related technical field expertise which is equivalent.
5+ years of hands-on experience in a DevOps, SRE, or infrastructure engineering role.
3+ years of experience specifically focused on automating and managing data infrastructure and pipelines.
1+ years of experience enabling AI features

Others:

Strong, demonstrable experience with Infrastructure as Code tools, particularly Terraform.
Strong background in DevOps principles and practices, and hands-on experience in building business intelligence solutions.
Highly experienced in automation, and problem-solving skills with proficiency in cloud technologies.
Ability to collaborate effectively with data engineers, analysts, and other stakeholders to ensure the reliability and performance of our data ecosystem.
Proven experience with dbt for data transformation, including developing models, tests, and managing dbt projects in a production environment.
Hands-on experience managing and optimizing Snowflake data warehouse environments.
Demonstrable experience with data modeling techniques for ODS, dimensional modeling (Facts, Dimensions), and semantic models for analytics and BI.
Strong proficiency in Python for automation, scripting, and data-related tasks. Experience with relevant Python libraries is a plus. Strong Bash scripting.
Solid understanding of CI/CD principles and tools (e.g., Bitbucket Runners, Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
Experience with cloud platforms (GCP preferred, AWS, or Azure) and their data services.
Experience with containerization technologies (e.g., Docker, Kubernetes) is a plus.
Knowledge of data integration tools and ETL/ELT concepts.
Familiarity with monitoring and logging tools.
Strong SQL skills.
Ability to work independently and as part of a collaborative team in an agile environment.
Strong communication skills, with the ability to explain complex technical concepts clearly."
Fully remote.
Flexible timings. You decide your work scheduled.
Market competitive compensation (in $$).
Insane learning and growth

Details

Location

Brazil

Experience

Min. 3 years3+ years of experience specifically focused on automating and managing data infrastructure and pipelines; 5+ years of hands-on experience in a DevOps, SRE, or infrastructure engineering role; 1+ years enabling AI features

Technologies

Job Responsibilities:

Design, build, and maintain scalable and resilient CI/CD pipelines for data applications and infrastructure, with a focus on Snowflake, dbt, and related data tools.
Implement and manage Snowflake dbt projects for data transformation, including developing dbt models, tests, and documentation, and integrating dbt into CI/CD workflows.
Develop and manage infrastructure as code (IaC) using Terraform to provision and configure cloud resources for data storage, processing, and analytics on GCP.
Automate the deployment, monitoring, and management of Snowflake data warehouse environments, ensuring optimal performance, security, and cost-effectiveness.
Collaborate with data engineers and data scientists to understand their requirements and provide robust, automated solutions for data ingestion, processing, and delivery.
Implement and manage monitoring, logging, and alerting systems for data pipelines and infrastructure to ensure high availability and proactive issue resolution.
Develop and maintain robust automation scripts and tools, primarily using Python, to streamline operational tasks, manage data pipelines, and improve efficiency; Bash scripting for system-level tasks is also required.
Ensure security best practices are implemented and maintained across the data infrastructure and pipelines.
Troubleshoot and resolve issues related to data infrastructure, pipelines, and deployments in a timely manner.
Participate in code reviews for infrastructure code, dbt models, and automation scripts.
Document system architectures, configurations, and operational procedures.
Stay current with emerging DevOps technologies, data engineering tools, and cloud best practices, particularly related to Snowflake, dbt, and Terraform.
Optimize data pipelines for performance, scalability, and cost.
Support and contribute to data governance and data quality initiatives from an operational perspective.
Help implement AI features
Bachelor's degree in Computer Science, Engineering, or a related technical field expertise which is equivalent.
5+ years of hands-on experience in a DevOps, SRE, or infrastructure engineering role.
3+ years of experience specifically focused on automating and managing data infrastructure and pipelines.
1+ years of experience enabling AI features

Others:

Strong, demonstrable experience with Infrastructure as Code tools, particularly Terraform.
Strong background in DevOps principles and practices, and hands-on experience in building business intelligence solutions.
Highly experienced in automation, and problem-solving skills with proficiency in cloud technologies.
Ability to collaborate effectively with data engineers, analysts, and other stakeholders to ensure the reliability and performance of our data ecosystem.
Proven experience with dbt for data transformation, including developing models, tests, and managing dbt projects in a production environment.
Hands-on experience managing and optimizing Snowflake data warehouse environments.
Demonstrable experience with data modeling techniques for ODS, dimensional modeling (Facts, Dimensions), and semantic models for analytics and BI.
Strong proficiency in Python for automation, scripting, and data-related tasks. Experience with relevant Python libraries is a plus. Strong Bash scripting.
Solid understanding of CI/CD principles and tools (e.g., Bitbucket Runners, Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
Experience with cloud platforms (GCP preferred, AWS, or Azure) and their data services.
Experience with containerization technologies (e.g., Docker, Kubernetes) is a plus.
Knowledge of data integration tools and ETL/ELT concepts.
Familiarity with monitoring and logging tools.
Strong SQL skills.
Ability to work independently and as part of a collaborative team in an agile environment.
Strong communication skills, with the ability to explain complex technical concepts clearly."
Fully remote.
Flexible timings. You decide your work scheduled.
Market competitive compensation (in $$).
Insane learning and growth