Senior Data Engineer, New York

As a Senior Data Engineer at Lab49/ION, your primary responsibilities will include analyzing, designing, coding, testing, and modifying software solutions to enhance our data platforms. You will also be tasked with building and managing automated delivery pipelines, optimizing data pipelines using technologies like Airflow, Snowflake, and AWS, and collaborating with stakeholders to elevate both business and technical requirements.

To qualify for the Senior Data Engineer position at Lab49/ION, candidates should have at least 8 years of experience in delivering data-centric platforms, particularly with large datasets. Proficiency in Python, strong expertise with AWS, Airflow, and Snowflake, along with a solid understanding of market and credit counterparty risk platforms, are mandatory.

Lab49/ION fosters a collaborative work environment by encouraging teamwork and effective communication among its staff members. As a Senior Data Engineer, you will engage closely with the Data Warehouse Architect and other stakeholders, ensuring that all platforms and solutions are developed in alignment with technical and business objectives.

At Lab49/ION, the salary range for a Senior Data Engineer varies based on experience and skill level, typically falling between $140,000 and $210,000. This range is complemented by a total compensation package that may also include performance-based bonuses, ensuring competitive remuneration for the vital role you’ll play.

Agile methodologies are significant for the Senior Data Engineer role at Lab49/ION as they promote flexibility and rapid delivery in software development. Being comfortable in an agile environment allows you to adapt to changing project requirements while effectively collaborating with cross-functional teams to deliver high-quality, scalable solutions.

In your response, highlight specific data engineering tools and technologies you've worked with, such as Python, Airflow, Snowflake, and AWS. Discuss any projects where you've implemented these technologies and the impact your work had on data processing or analytics.

Discuss your approach to implementing monitoring, alerting, logging, and tracing systems. Provide examples of tools you’ve used to ensure durability and performance, and any metrics that you regularly track to gauge system health.

Explain your methodology for maintaining high data quality standards through validation checks, unit tests, and integration tests. Share specific instances where you've identified and resolved data quality issues in large datasets.

Provide details about the CI/CD pipelines you've created, focusing on the tools you used and the automation processes implemented. Illustrate how these pipelines improved your team's productivity, efficiency, or deployment frequency.

Share your experience collaborating with other team members, such as Data Warehouse Architects. Discuss communication strategies you use to ensure alignment on project objectives, as well as how you handle any conflicts that may arise during collaboration.

Discuss the specific tools and technologies you’ve employed to manage large datasets effectively, such as Snowflake or AWS. Explain your reasoning for using these tools and any benefits you’ve observed from their use.

Provide an example of a complex issue you faced in data engineering, detailing the problem, the steps you took to resolve it, and the outcome. Show your problem-solving skills and ability to think critically under pressure.

Explain your practices for building maintainable and scalable software, such as writing clean code, proper documentation, and using modular architectures. Provide examples that underline your commitment to best practices in software engineering.

Discuss your familiarity with agile methodologies, including any frameworks you’ve used (such as Scrum or Kanban). Share how working in such an environment has benefitted your projects and how you adapt to changes dynamically.

Articulate the importance of documentation in your workflow, including the types of documentation you produce and maintain. Emphasize how good documentation enhances team collaboration, aids in onboarding, and contributes to project success.