Data Engineer - ML Systems

Introduction to Demandbase: 

Demandbase helps B2B companies hit their revenue goals using fewer resources. How? By using the power of AI to identify and engage the accounts and buying groups most likely to purchase. Our account-based technology unites sales and marketing teams around insights that you can understand and facilitates quick actions across systems and channels to deliver big wins. It’s flexible, scalable ABM built for you.

As a company, we prioritize both the advancement of careers and the development of world-class technology. We invest heavily in people, our culture, and the communities around us. We have offices strategically located in San Francisco and New York in the US, and Hyderabad, in India and we embrace a hybrid work model in these regions. Outside of these areas we offer a remote work option and boast a significant presence in Austin, TX, Atlanta, GA, and London, UK. Continuously lauded as a great place to work, we are Great Place to Work Certified, and have earned distinctions such as "Fortune's Best Workplaces in the Bay Area,"Best Workplaces in Technology," "Best Workplaces for Millennials," and "Best Workplaces for Parents"!

We're committed to attracting, developing, retaining, and promoting a diverse workforce. By ensuring that every Demandbase employee is able to bring a diversity of talents to work, we're increasingly capable of achieving our mission to transform the way B2B companies go to market. We encourage people from historically underrepresented backgrounds and all walks of life to apply. Come grow with us at Demandbase!

About the Role:

As a Data Engineer on Demandbase's AI team, you will play a critical role in designing, developing, and optimizing data pipelines that drive machine learning systems. You will collaborate with cross-functional teams, including data scientists, engineers, and cloud operations, to enhance the performance of machine learning models and ensure robust infrastructure. The role involves working with large-scale datasets, improving data reliability, and adhering to industry-leading standards in cloud technologies, DevOps practices, and machine learning engineering. You'll also contribute to innovations in ML Ops and prompt engineering, applying cutting-edge techniques to improve system scalability and efficiency.

The base compensation range for this position, not including the company bonus, is: $145,000-$198,000

What you’ll be doing:

• Design, develop, and maintain efficient data pipelines and ETL processes.
• Raise the standards by improving test coverage and automation.
• Work with cloud operations and QA teams to implement industry-standard build, test, and deploy pipelines.
• Implement indexing, partitioning, and tuning strategies to improve data model performance.
• Collaborate with data engineers, data scientists, data platform experts, front-end developers, and analytics teams to align on priorities and deliverables.
• Drive continuous improvement in release processes to achieve best-in-class standards..
• On-call rotation participation

What we’re looking for:

• Minimum 5 years of relevant work experience as a Data Engineer or ML Engineer, or 3 years with a Master's degree in Computer Science or related field.
• Strong proficiency in Object-Oriented Programming (OOP) using Scala/Java and Python.
• Experience building unit tests, dockerized integration tests, CI/CD, and repository management.
• Ability to deploy, monitor, and manage software, particularly ML models.
• Experience in ETL/ELT data pipelines for large datasets. Proficiency in both batch and streaming processes using Spark is preferred. 
• Solid understanding of advanced SQL techniques for querying, transformation, and performance optimization.
• Ability to deploy, monitor, and manage software, particularly ML models.
• Expertise in building, deploying, and optimizing DAGs in Apache Airflow or a similar tool.
• Experience with Docker, Kubernetes, and cloud platforms such as AWS (preferred) (IAM, EC2, S3, terraform) or GCP.
• Good communication skills.

Preferred experience:

• Experience working on large-scale B2B and SaaS applications with tenant-based architectures.
• Proficiency in ML Ops practices, including experience with A/B testing technologies and frameworks.
• Expertise in crafting, testing, and refining prompts for effective communication with LLMs like GPT. Ability to generate accurate and contextually relevant responses.

Benefits:

We offer a comprehensive benefits package designed to support your health, well-being, and financial securit