DataOps Engineer

As a DataOps Engineer at Leonardo.ai, your primary responsibilities include extending and scaling our data processes within the AWS data lakehouse, providing training and support to our existing team, advising on data architecture, and maintaining a comprehensive view of the data lifecycle. You'll also be tasked with building high-quality database systems and performing regular evaluations to ensure data security and integrity.

To apply for the DataOps Engineer position at Leonardo.ai, candidates should have hands-on experience with AWS tools like S3, Glue, and Redshift, along with strong database skills in PostgreSQL. Familiarity with programming languages such as JavaScript, Python, and Terraform is also advantageous. A deep understanding of data architecture and best practices in data management is essential.

At Leonardo.ai, a DataOps Engineer can expect an inclusive, supportive, and collaborative work environment. We value diversity and strive to create a safe space where team members can be authentic. You'll find a flexible work culture that encourages professional development and offers benefits like remote working options and fun company events.

A DataOps Engineer at Leonardo.ai will primarily use AWS tools—such as S3 for storage, Glue for ETL, and Redshift for data warehousing. You will also work with PostgreSQL for database management, as well as programming languages like JavaScript and Python to support your tasks in building and maintaining our data systems.

Leonardo.ai offers numerous growth opportunities for a DataOps Engineer. With ongoing training and upskilling initiatives, you will have the chance to expand your expertise in data architecture, cloud engineering, and best practices. Additionally, by collaborating within a small team, you'll gain hands-on experience and strategic insight that can propel your career to new heights.

When answering this question, focus on specific projects where you've utilized AWS services like S3, Glue, or Redshift. Highlight what challenges you faced, how you solved them, and the impact your work had on data accessibility and quality.

In your response, discuss methods you've employed to maintain data integrity, such as data validation checks and access controls. Emphasize familiarity with best practices that safeguard sensitive data while enabling necessary access for users.

Your answer should outline the steps you take when designing data models, including requirements gathering, defining relationships, and ensuring scalability. Provide an example of a successful data architecture project you've worked on, detailing the decisions you made.

Detail your proficiency in JavaScript, Python, or other relevant languages, along with specific scenarios in which you've used these skills to develop data processing scripts, run queries, or support data visualization.

Explain your methods for staying informed, such as following industry blogs, participating in webinars, or joining online communities. Mention any recent technologies or methodologies you've adopted and how they can benefit your work.

Share a specific instance where you provided training or support on a data task. Emphasize your teaching methods, the challenges faced, and the successful outcome of your efforts.

Discuss fundamental metrics such as throughput, data latency, and error rates. Explain how these metrics can impact user experience and why monitoring them is crucial for maintaining optimal data operations.

Describe your step-by-step process for identifying, investigating, and resolving data discrepancies. Highlight the importance of documentation and communication with team members during this process.

Your answer should include strategies like data lifecycle policies, transitioning infrequently accessed data to cheaper storage classes, and regularly reviewing usage. Discuss your experience in balancing cost optimization with performance needs.

Articulate the importance of a DataOps Engineer as a bridge between data engineering, operations, and analytics. Emphasize how this role contributes to enhancing data accessibility and ensuring efficient data management across the organization.