ENGINEER-DATA

As an Engineer-Data at HMH, your primary responsibilities will include selecting and designing data platforms that meet diverse business use cases, collaborating with Data Architects and the IT team to achieve project goals, and ensuring the deployment of scalable data management systems. You'll also monitor performance metrics and propose infrastructure improvements while defining data retention, security policies, and governance measures.

To qualify for the Engineer-Data position at HMH, you should hold a Bachelor's or Master's degree in computer science, software engineering, or a related field. Ideal candidates will have experience with SQL-based technologies, data modeling, and governance. Familiarity with NoSQL databases and programming languages such as Python or JAVA, as well as exposure to big data querying tools, will also be beneficial.

Yes, HMH values experience with digital marketing analytics tools such as Adobe or Google Analytics for the Engineer-Data role. Such expertise can enhance the data-driven strategies you develop, making your contributions even more impactful on our educational content and services.

An Engineer-Data at HMH should be proficient in at least two programming languages. Preferred languages include Python, Java, C/C++, and R. Your ability to work with these languages will be essential for handling data processing, analysis, and implementing machine learning techniques.

At HMH, you can expect a collaborative and agile work environment. We emphasize teamwork and communication among our staff, ensuring that everyone can contribute to project goals effectively while enjoying a supportive atmosphere. Your input will be valued as we work together to innovate educational content.

In answering this question, be sure to focus on your direct experience with data modeling projects, mentioning specific tools and frameworks you’ve used. Discuss your understanding of data governance principles and how you've implemented them in past roles to maintain data quality and compliance.

When discussing performance monitoring, emphasize your analytical approach. Describe specific metrics you track, tools you utilize for monitoring, and how you've implemented recommendations for improvement in previous positions to achieve optimal performance.

Here, you can provide insight into your programming expertise. Highlight your top languages, the projects you've used them in, and how they contributed to successful outcomes. Discuss reasons for your comfort level with these languages and your willingness to learn new ones.

Choose a specific example from your past experience where you encountered a challenging data issue. Explain the problem, the steps you took to analyze and resolve it, and the positive results that followed to showcase your problem-solving skills.

Discuss your strategies for implementing data security measures. Talk about your familiarity with data retention policies, encryption techniques, and how you work within compliance frameworks to protect sensitive information in your projects.

In your response, mention the big data querying tools you have experience with, such as Pig, Hive, or Impala. Explain specific scenarios in which you used these tools to extract meaningful insights and support data-driven decision-making.

Emphasize your organizational skills and methods for prioritizing tasks. You can mention tools like Kanban boards or project management software and how you adapt your prioritization strategy based on project deadlines and stakeholder needs.

In your answer, outline your familiarity with continuous integration/continuous deployment (CI/CD) practices and tools. Discuss how implementing CI/CD has improved the development cycle in your previous roles and enabled quicker iterations on data projects.

Focus on your background with statistical analysis techniques and specific predictive modeling projects you've undertaken. Provide examples of how these methodologies guided decision-making or forecasting outcomes in past work.

Address this question by describing your commitment to continuous learning. Mention resources you follow, such as blogs, webinars, or conferences, and any professional networks you participate in to stay informed about emerging trends and technologies in data engineering.