Cloud Data Engineer

US - MA - Boston


US - MA - Boston


Business Support, Product & Engineering

Rapid7 is a leading provider of security data and analytics solutions that enable organizations to implement an active, analytics-driven approach to cyber security. We combine our extensive experience in security data and analytics and deep insight into attacker behaviors and techniques to make sense of the wealth of data available to organizations about their IT environments and users. Our solutions empower organizations to prevent attacks by providing visibility into vulnerabilities and to rapidly detect compromises, respond to breaches, and correct the underlying causes of attacks. Rapid7 is trusted by more than 9000 organizations across 125 countries, including 52% of the Fortune 100. To learn more about Rapid7 or get involved in our threat research, visit


Job Overview

Rapid7 seeks a Cloud Data Engineer to build and maintain data infrastructure within the Business Intelligence team's data platform. You will be responsible for deploying data pipelines and machine learning models in the cloud, implementing DevOps practices and developing data models within Snowflake. You will work closely with AWS, Snowflake and DevOps applications such as Github Actions, Terraform, Jenkins and more. In this role you will bridge the gap between a data engineer and a DevOps engineer by building monitoring systems, cloud applications and CI/CD pipelines that support the data engineering team's efforts. 

The ideal candidate has  hands-on experience performing DevOps work in a cloud environment, and has worked closely with databases and data pipelines. It's critical that you are able to translate  business objectives into data required to support key analyses. You will collaborate with a creative, analytical and data-driven team to bring a single source of truth and self-service analytics to the entire company.


Essential Responsibilities

  • Transform Rapid7's Business Intelligence and Analytics data platform by applying DevOps best practices of automation, monitoring, CI/CD and configuration management

  • Build and maintain the applications that ingest, analyze and store Rapid7's enterprise data 

  • Productionize data and machine learning pipelines with docker containerization and clustering tools (ECS/Kubernetes) 

  • Develop effective monitoring and alerting systems to provide real-time visibility into the health of data infrastructure, cloud applications and data/machine learning pipelines 

  • Build an environment that enables data scientists to easily develop and productionize Python, R and Spark code on top of a Snowflake data warehouse

  • Automate existing code and processes using scripting, CI/CD, infrastructure-as-code and configuration management tools 

  • Perform data engineering projects within Snowflake such as developing data pipelines, data models and metadata management solutions

  • Collaborate with stakeholders in product, business and IT to deliver data products

  • Work closely with leadership to drive adoption of the latest DevOps and DataOps trends and technologies. 

  • Partner with the IT, Infrastructure and engineering teams on  integration efforts between systems that impact data & Analytics

Qualifications and Skill Requirements

  • BS or MS in Computer Science, Analytics, Statistics, Informatics, Information Systems or another   quantitative field. or equivalent experience and certifications will be considered

  • Strong written and verbal communication skills 

  • Highly collaborative in working teammates and stakeholders 

  • More than 1 year of experience with a major cloud provider (AWS, Azure, GCP)

  • More than 1 year of experience working with relational databases (Snowflake, Redshift, BiqQuery, Postgres, MySQL etc.) and SQL

  • Proficiency in one object oriented language such as Python, Java, Scala etc. 

  • Experience with a CI/CD tool such as Jenkins, Github Actions, Spinnaker, GitLab 

  • Experience with an infrastructure-as-code tool such as Terraform or CloudFormation 

  • Experience deploying code in cloud environments using tools such as Docker, Kubernetes, EC2 etc. 

  • Working knowledge of data architecture, data warehousing, and metadata management