Senior 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 Senior 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 compensation range for this position: $190,000 - $210,000 

What you’ll be doing:

Data Pipeline Development and Optimization:

• Design, develop, and maintain efficient data pipelines and ETL processes.
• Implement indexing, partitioning, and tuning strategies to improve data model performance.
• Reduce costs, improve landing times, and ensure pipeline reliability.

Machine Learning Model Improvement:

• Debug and enhance machine learning models for optimal performance.

Cross-Functional Collaboration:

• Collaborate with data engineers, data scientists, data platform experts, front-end developers, and analytics teams to align on priorities and deliverables.

Infrastructure and Quality Assurance:

• Work with cloud operations and QA teams to implement industry-standard build, test, and deploy pipelines.
• Drive continuous improvement in release processes to achieve best-in-class standards.

System Optimizations:

• Optimize existing systems and solutions to align with industry best practices.

 

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, Electrical Engineering, or a related field.
• Strong proficiency in Object-Oriented Programming (OOP) using Scala/Java and Python.
• Solid understanding of advanced SQL techniques for querying, transformation, and performance optimization.
• Ability to deploy, monitor, and manage software, particularly ML models.
• Experience in designing, building, and optimizing ETL/ELT data pipelines for large datasets. Proficiency in both batch and streaming processes is preferred.
• Expertise in building, deploying, and optimizing DAGs in Apache Airflow or a similar tool.
• Proficiency in Gitlab for version control, branching, and collaboration.
• Understanding of best practices for data modeling, including star schemas, snowflake schemas, and data normalization techniques. 
• Ability to work effectively with data scientists, analysts, and stakeholders to translate business requirements into technical solutions.
• Solid documentation skills for pipeline design and data flow diagrams.