Data Engineer

As a Data Engineer at MANTL, you'll be responsible for owning and managing data integrations from design through to their launch. Your duties will include developing real-time data flows for crucial banking systems, collaborating with other teams to enhance data tool usability, and resolving support tickets swiftly. You'll also focus on keeping integrations robust while driving revenue through activation, ensuring that MANTL continues to deliver exceptional value to our community bank and credit union clients.

To qualify for the Data Engineer role at MANTL, candidates should have a minimum of three years of experience writing production-level code in Python and developing data pipelines. Experience in architecting solutions using tools like Airflow and DBT is essential. Additionally, candidates should possess strong skills in SQL and a solid understanding of high-quality integration processes, although domain knowledge is not mandatory, passion, curiosity, and problem-solving skills are highly valued.

In the Data Engineer role at MANTL, you'll work with a range of technologies, primarily focusing on Python for coding, Airflow for orchestration, and DBT for data transformation. Additionally, SQL will be a key part of your toolkit, helping you create robust data pipelines and perform necessary tweaks. Expect to collaborate closely with development teams, ensuring seamless API integration and valuable data flow across platforms.

Data Engineers at MANTL enjoy a variety of benefits, including a competitive salary ranging from $140,000 to $165,000, equity options, and comprehensive medical, dental, and vision insurance. You'll also receive a remote setup allowance, unlimited PTO, and access to mental health programs. The vibrant team culture fosters fun events like wine tastings and happy hours, ensuring you enjoy your work-life balance in a supportive environment.

The work culture for Data Engineers at MANTL is collaborative, inclusive, and dynamic. We place a high value on treating each other with respect and accountability. Our team is composed of passionate technologists who engage in productive discussions and continuously strive for improvement while tackling complex challenges. We believe in a balanced work-life integration and cherish the creativity that each member brings to the table.

Discuss specific projects where you utilized Python to build data pipelines or automate processes. Give examples of SQL queries you've written to manipulate or retrieve data, and mention any performance enhancements you've made in your queries. It's vital to emphasize both the scale and impact of the work you've done in previous roles.

Start by explaining your methodology for gathering requirements from stakeholders to understand integration goals. Detail your approach to designing scalable data architectures, selecting the right tools, and testing integration points. Emphasize the importance of documentation and monitoring for long-term maintainability.

Share a specific challenge you encountered, such as slow query performance. Detail the steps you took to diagnose the issue, such as analyzing execution plans or identifying bottlenecks, and discuss the solutions you implemented, like indexing strategies or query refactoring, to successfully optimize performance.

Discuss the various testing strategies you employ to validate data integrity, including unit tests and data validation queries. Highlight your experience with monitoring practices that allow you to identify anomalies or errors in data flows, and how you implement logging to trace issues back to their source.

Provide a clear example of a data integration problem you faced, explaining the steps you took to identify the root cause. Discuss how you collaborated with team members or leveraged specific tools to resolve the issue, ultimately restoring functionality and improving processes to prevent future occurrences.

Mention specific resources you use, such as blogs, podcasts, online courses, or industry conferences. Discuss how you apply new learnings in your projects and your enthusiasm for adopting innovative tools and practices to enhance your data engineering work.

Emphasize the importance of collaboration in producing high-quality data integrations. Share examples of how your interactions with product managers, designers, and other engineers have led to better solutions. Stress that open communication fosters a better understanding of project objectives and team dynamics.

Talk about specific projects where you implemented Airflow to manage data workflows. Describe how you utilized DAGs to schedule and monitor tasks, and mention any challenges you overcame to ensure smooth data pipeline operations. Highlight the benefits you witnessed in automation and error handling.

Discuss any cloud platforms you've used, such as AWS, Azure, or GCP, explaining how you've deployed data solutions in the cloud. Provide insights on security, scalability, and cost-efficiency considerations you've faced, and mention any relevant certifications or training you've obtained in cloud technologies.

Explain the version control systems you utilize, such as Git, to manage code changes within your data engineering projects. Describe your branching strategy, how you conduct code reviews, and the importance of maintaining clear commit messages for transparency and collaboration.