Data & ML Platform Engineer (Hybrid)

As a Data & ML Platform Engineer at Homebase, your main responsibilities include designing and developing data ingestion processes, collaborating on data architecture transformations using Databricks, guiding the implementation of ML model training and deployment, and driving improvements in data and ML workflows. You'll also take ownership of projects and work closely with cross-functional teams to translate business requirements into technical solutions.

To be considered for the Data & ML Platform Engineer role at Homebase, you'll need at least 4 years of software development experience, especially in data and machine learning. Proficiency in SQL, Python, and Databricks is essential, alongside familiarity with Airflow, Kafka, and Redshift. Experience with data architecture transformations and building robust machine learning models will give you a significant advantage.

At Homebase, we value continuous learning and growth for all our engineers, including those in the Data & ML Platform Engineer role. You'll have opportunities to engage in advanced training, attend conferences, and collaborate with seasoned industry experts. We also encourage our team members to explore innovative ideas and drive improvements in their workflows, ensuring everyone can learn and evolve in their careers.

A Data & ML Platform Engineer at Homebase should be proficient in SQL, Python, and Databricks, as these tools are instrumental in our data architecture and ML model deployment strategy. Familiarity with frameworks such as Airflow for orchestration and real-time processing tools like Kafka and Redshift for data storage is also crucial for success in this role.

Collaboration is at the heart of the Data & ML Platform Engineer position at Homebase. You'll partner with cross-functional teams, including data engineers, data scientists, and product teams, to gather business requirements and translate them into scalable technical solutions. This teamwork ensures the successful execution of projects and fosters a culture of innovation and shared success.

When answering this question, emphasize specific data ingestion projects you've worked on, the technologies you used, such as Databricks and SQL, and any challenges you faced. Highlight your problem-solving skills and how you optimized those processes to improve data quality and efficiency.

Discuss the machine learning frameworks you're familiar with, such as TensorFlow or PyTorch, and provide examples of how you've built and deployed models. It's helpful to describe real-world scenarios where you've utilized these frameworks to deliver value, focusing on the impact on business outcomes.

Talk about best practices for building scalable data pipelines, such as using cloud services, designing for high availability, and employing monitoring tools to track performance. Share any specific tools or techniques you utilized to achieve these goals in your previous projects.

Use the STAR (Situation, Task, Action, Result) method to outline your experience, focusing on how you identified inefficiencies and the steps you took to improve the deployment process. Discuss any specific changes made and the measurable outcomes that resulted from your efforts.

Emphasize your communication skills and any visualization techniques you've used to explain complex data processes or concepts. Discuss your approach to gathering requirements and ensuring alignment among stakeholders, which is crucial for developing effective technical solutions.

In your response, highlight your experience with CI/CD pipelines specific to ML, mentioning tools like Jenkins or GitLab CI. Discuss the specific steps you took to automate the deployment process and any resulting improvements in deployment speed or reliability.

Discuss your methodology for data modeling, such as understanding business needs, determining data relationships, and ensuring scalability. Illustrate your process with a specific example that demonstrates your analytical capabilities and attention to detail.

Provide examples of how you've implemented feedback loops, automated processes, or adopted new technologies to enhance existing ML workflows. It's essential to showcase your proactive approach and willingness to experiment with innovative solutions.

Share your methods for staying informed, whether through online courses, attending conferences, or engaging with professional communities. Highlight specific resources you rely on and how you integrate new knowledge into your work.

Utilize the STAR method once more to illustrate a challenging technical issue you've encountered. Discuss the thought process behind your solution, the tools you used, and the ultimate results of your actions.