Senior Staff Data Engineer

As a Senior Staff Data Engineer at Kandji, your core responsibilities revolve around designing and implementing robust data pipelines. You'll serve as a subject matter expert for new data integrations, automate manual processes, and optimize how data is delivered across the business. Collaboration is key, as you'll engage with various teams to understand their data needs while driving the technological advancements of data systems.

To be considered for the Senior Staff Data Engineer role at Kandji, you'll need at least 6 years of experience in data engineering, particularly in building large-scale distributed platforms. Proficiency in Python and SQL is essential, along with familiarity with tools like Apache Kafka or Kinesis. Experience in data modeling, especially with dbt and technical leadership in data architecture, will set you apart.

Kandji fosters an inclusive and collaborative team culture, where diverse perspectives are valued. As a Senior Staff Data Engineer, you'll be part of a high-performing team that emphasizes innovation and technical excellence. We believe that strong teams are diverse teams, and we actively encourage sharing unique insights that contribute to our collective success.

Absolutely! At Kandji, we prioritize career development and growth for our employees. In the Senior Staff Data Engineer position, you'll have exciting opportunities to lead innovative projects that challenge your skills while paving your path forward in your career. You'll also have access to mentorship and resources to facilitate your professional journey.

The Senior Staff Data Engineer position at Kandji is an on-site role based in our Miami office (Coral Gables). Our team works collaboratively in person five days a week, which enhances communication and teamwork as we tackle challenging data projects.

When answering this question, consider discussing specific projects where you've implemented data modeling techniques. Highlight your familiarity with dbt and any unique approaches you've taken to model both structured and unstructured datasets effectively.

Outline the methods you employ to maintain data quality, such as implementing automated testing, data validation checks, and monitoring processes to catch any discrepancies early on. Sharing examples of past challenges and how you resolved them can help illustrate your problem-solving capabilities.

Collaboration is essential in data engineering. You could express how you prioritize understanding different teams' unique requirements, actively listen to their challenges, and tailor your solutions to meet their needs. Emphasize communication as a cornerstone of successful collaborations.

Discuss your proficiency with workflow management systems like Airflow or Argo Workflows. Provide context around how you used these tools to streamline data pipelines, facilitating both reliability and timely delivery of data to stakeholders.

Speak about the platforms you've worked with, the third-party data sources you've integrated, and the structure you've created for easy access and usability. Mention any innovative strategies you've implemented to enhance ingestion processes effectively.

Use this question to showcase your problem-solving skills by sharing specific challenges you've faced, such as performance bottlenecks or data inconsistencies. Explain the steps you took to identify the issues and how you collaborated with your team to find solutions.

Express your passion for continuous learning by highlighting the resources you utilize to stay informed, such as blogs, webinars, and community forums. Discuss any specific technologies or trends you're interested in and how they could impact Kandji's data engineering efforts.

In your response, consider outlining a systematic approach, including analyzing execution times, identifying bottlenecks, and potentially refactoring the code or optimizing queries. Mention any tools you might use for monitoring and profiling performance.

Talk about your familiarity with various data serialization formats like Avro, Parquet, JSON, and Protobuf. Discuss how you choose the appropriate format based on specific use cases or performance requirements.

Detail the strategies you use to ensure you can monitor data flows effectively. Discuss implementing logging, usage metrics, and alerts to detect anomalies early and ensure a reliable data environment in your role as a Senior Staff Data Engineer.