Analytics Engineer II

As an Analytics Engineer II at Curaleaf, your primary responsibilities include building and maintaining scalable data pipelines, using tools like dbt and SQL to ensure accurate data ingestion and organization. You'll collaborate with other data teams to deliver optimized data solutions, enhance data quality practices, and ensure that our analytics platforms are reliable and accessible for decision-makers.

To qualify as an Analytics Engineer II at Curaleaf, you need 4 to 7 years of experience in data analytics or engineering, alongside proficiency in SQL, Python, or R. A solid understanding of data workflows and best practices is crucial. Additionally, familiarity with big data technologies, cloud platforms like Snowflake, and version control tools like Git will be advantageous.

Curaleaf is committed to fostering career growth for its employees. As an Analytics Engineer II, you'll have access to various professional development opportunities, including training programs, mentorship, and the ability to work on diverse projects that enhance your skillset and career trajectory within the rapidly evolving cannabis industry.

As an Analytics Engineer II at Curaleaf, you will primarily use tools like dbt, SQL, and Python to build and optimize data pipelines. Familiarity with cloud platforms, especially Snowflake, and version control systems such as Git will also be essential for your daily tasks.

The work culture at Curaleaf is collaborative, innovative, and purpose-driven. As an Analytics Engineer II, you will be part of a team that values diversity, teamwork, and a commitment to social responsibility, encouraging a positive and impactful work environment.

Be prepared to discuss star schema and snowflake schema models, including how you have implemented them in previous projects to organize data for effective reporting and analysis.

Discuss strategies such as indexing, analyzing query execution plans, optimizing joins, and eliminating unnecessary columns in SELECT statements. Use examples from your past experience to illustrate your approach.

Talk about employing validation checks, data cleaning methods, and best practices for documentation to maintain accuracy and reliability in the datasets you work with.

Share specific details about the project, the challenges faced, and the innovative solutions you implemented, focusing on teamwork, problem-solving, and results achieved.

Discuss your methods for professional development, such as attending workshops, participating in webinars, and following industry publications to stay informed about advancements and best practices.

Explain your experience using Git for collaborative projects, focusing on how you've managed branches, conducted code reviews, and implemented version control strategies in your data work.

Demonstrate your organizational skills and the methods you use for managing priorities effectively, such as setting deadlines, using project management tools, and communicating with stakeholders.

Explain your approach to engaging with stakeholders, how you identify their needs, document requirements, and ensure that the data solutions delivered align with their objectives.

Discuss how important documentation is for reproducibility, clarity, and fostering collaboration among peers, sharing your strategies for maintaining comprehensive project documentation.

Share how you leverage constructive feedback positively to enhance your analysis, communicate changes effectively, and constantly improve your data solutions through continuous learning.