Senior Data Engineer (MLOps)

Summary

Join Massive Rocket, a rapidly growing Martech agency specializing in Braze and Snowflake, as a Data Engineer. You will play a key role in designing, implementing, and maintaining robust data systems. This position requires strong expertise in data pipeline orchestration, data lake/warehouse design, and MLOps. You will collaborate with engineering and product teams to deliver cutting-edge AI and data solutions. The role offers a fully remote, flexible work environment with opportunities for career advancement and team events. Massive Rocket values innovation and a supportive work culture.

Requirements

• 5+ years of experience in data engineering or a related field
• Strong expertise in data pipeline orchestration tools such as Apache Airflow
• Proven track record of designing and implementing data lakes and warehouses (experience with Azure is a plus)
• Solid understanding of MLOps practices, including model training, deployment, and monitoring
• Proficiency in programming languages such as Python & SQL
• Experience with distributed computing frameworks such as Spark
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines
• Effective communicator and team player, comfortable working with cross-functional teams to deliver high-quality solutions
• Experience working in an agency setting with clients
• Strong communication skills with professional level of proficiency in english

Responsibilities

• Design and implement scalable, secure, and high-performance data lake and data warehouse solutions
• Leverage best practices in schema design, partitioning, and optimisation for efficient storage and retrieval
• Build and maintain data models to support analytics and machine learning workflows
• Develop, monitor, and optimize ETL/ELT workflows using Apache Airflow
• Ensure data pipelines are robust, error-tolerant, and scalable for real-time and batch processing
• Develop and maintain scalable web scraping solutions to collect data from diverse sources, including APIs, websites, and other unstructured data sources
• Extract, clean, and transform unstructured data such as text, images, and log files into structured formats suitable for analysis
• Use tools and frameworks like BeautifulSoup, Scrapy, or Selenium for web scraping, and natural language processing (NLP) techniques for text processing
• Design and implement cloud-native data solutions with Microsoft Azure
• Optimize costs and performance of cloud-based data solutions
• Use Terraform to automate the provisioning and management of cloud infrastructure
• Define reusable and modular Terraform configurations to support scalable deployment of resources
• Collaborate with data scientists and machine learning engineers to operationalise machine learning models
• Implement CI/CD pipelines for machine learning workflows, ensuring efficient model deployment and monitoring
• Utilize Kubernetes and containerisation technologies (e.g., Docker) to deploy scalable, fault-tolerant data processing systems
• Manage infrastructure and resource allocation for containerised data applications
• Collaborate effectively with developers and other stakeholders to understand their needs and provide appropriate platform solutions
• Maintain comprehensive documentation of platform architecture, processes, and procedures

Preferred Qualifications

• Demonstrated experience with Terraform for infrastructure provisioning and management
• Hands-on experience with Kubernetes and containerised environments
• Experience in the healthcare or medical industry
• Familiarity with compliance standards like HIPAA

Benefits

• Fast-moving environment – you will never stop learning and growing
• Supportive and positive work culture with an emphasis on our values
• International presence – work with team members in Europe, the US, and around the globe
• 100% remote forever
• Flexible Vacation Policy
• Career progression paths and opportunities for promotion/advancement
• Organised team events and outings