Senior Data Engineer

As a Senior Data Engineer at Coinme, your responsibilities include leading the design, development, and implementation of data solutions, developing scalable data pipelines, optimizing performance for reliability, and collaborating with other teams on data architecture and analytics projects.

To qualify for the Senior Data Engineer position at Coinme, candidates should possess a Bachelor’s or Master’s degree in Engineering, Computer Science, or related fields, alongside a minimum of 7 years of experience with ETL, SQL, and data warehousing technologies such as Snowflake, Apache Kafka, and familiarity with Python or R.

Coinme offers a fully remote work environment for the Senior Data Engineer position, allowing you to work from anywhere within Canada while complying with time zones such as PST, CST, MST, or EST. This flexibility supports a culture of collaboration and innovation.

A Senior Data Engineer at Coinme significantly contributes to the company's mission by enhancing the data infrastructure, enabling more effective business decision-making, and driving the overall improvement in data quality and accessibility through innovative analytics solutions.

While a strong foundation in ETL, SQL, and data warehousing is essential, preferred skills for the Senior Data Engineer at Coinme include experience with cloud platforms like AWS, machine learning, financial systems, and a passion for cryptocurrency and blockchain technology.

When answering this question, highlight specific ETL toolsets you’ve worked with, discuss the pipelines you've designed, and elaborate on the results achieved by optimizing these processes for performance and reliability.

Share your experience with data quality management processes, including data correction, validation strategies, and any tools you’ve implemented to maintain data integrity across all stages of the data pipeline.

Discuss various architectural frameworks you've utilized, how you assess scalability challenges, and how you ensure that solutions are able to grow with business needs while maintaining performance.

Describe the project scope, your leadership role, the collaborative efforts involved, and the end results. Emphasize how you motivated team members and resolved any conflicts during the project lifecycle.

Mention specific tools like Apache Kafka, Snowflake, or PowerBI, and explain why you choose these technologies based on experience, performance, or ease of integration within projects.

Describe the processes you've implemented for logging, monitoring, and alerting on data pipeline performance issues. This can include specific tools and practices that help you proactively manage data solutions.

Emphasize the importance of maintaining comprehensive documentation for all data flows, ETL processes, and architectural decisions. Explain how this practice aids onboarding, knowledge sharing, and operational continuity.

Share your strategies for effective communication with data scientists and product managers, such as cross-functional meetings and regular updates, and how this collaboration impacts project success.

Provide a detailed account of a specific project, including the challenges, your design approach, the technologies used, and the positive outcomes achieved for the team or organization.

Discuss how you’ve adapted to Agile principles in your workflow, how it’s improved collaboration and productivity, and your contributions to sprint planning, reviews, and retrospectives as a Senior Data Engineer.