Analytics Engineer

As an Analytics Engineer at Curaleaf, you will build and maintain scalable data pipelines using tools like dbt, SQL, and Python. You'll be responsible for creating trusted datasets for reporting and analysis, driving the development of analytics platforms that ensure data accessibility and reliability across teams, improving data quality and performance, and collaborating with various stakeholders to turn business needs into optimized data solutions.

To qualify for the Analytics Engineer position at Curaleaf, you should have 1-5 years of experience in a data analytics or engineering role, with strong knowledge of data workflows and practices. A bachelor's or master’s degree in a related field, proficiency in SQL and Python (or similar languages), and experience with dbt or similar transformation frameworks are essential requirements. Familiarity with big data technologies and cloud platforms, especially Snowflake, is also a plus.

At Curaleaf, we value the career growth of our employees, including Analytics Engineers. We provide various career development opportunities, mentorship programs, and supportive resources to enhance your skills. As part of a collaborative Data Team, you'll be continuously developing your expertise in innovative technologies and practices in the fast-paced cannabis industry, positioning you for future advancement.

In your role as an Analytics Engineer at Curaleaf, you will primarily use tools like dbt for data transformation, SQL for querying databases, and Python for data manipulation and analysis. You will also work with version control systems like Git and CI/CD practices to ensure reproducibility and collaborative development of data solutions.

At Curaleaf, you'll enjoy a dynamic and innovative work environment that encourages collaboration and creativity. Working as part of a fast-paced Data Team, your contributions will have a real impact on decision-making processes across the organization. We prioritize a culture that fosters growth, team spirit, and a commitment to corporate social responsibility.

In my previous roles, I have designed and maintained scalable data pipelines using tools like dbt and SQL. I focused on ingesting and transforming data from various sources, ensuring that the datasets were clean and reliable. I also implemented version control with Git to ensure collaborative data processes.

To ensure data integrity, I emphasize rigorous data validation and documentation practices throughout the data transformation process. I regularly conduct quality checks on the source data and maintain clear logs of changes, which helps in troubleshooting and maintaining data trustworthiness.

When optimizing SQL queries, I analyze query execution plans to identify performance bottlenecks. I use indexing effectively, reduce unnecessary computations, and ensure that I write efficient joins. My goal is to enhance performance while minimizing costs, especially when dealing with large datasets.

I have worked extensively with Snowflake in my previous positions, where I leveraged its capabilities for storing and processing large volumes of data. I have experience integrating Snowflake with various ETL tools, optimizing data storage, and ensuring that performance is consistent even with scaling workloads.

I focus on simplifying complex concepts into digestible information. Using data visualization tools, I present insights through clear charts and graphs, while also preparing concise explanations that point out key findings and their implications for business decisions.

In a recent project, I collaborated closely with marketing and sales teams to understand their data needs. My approach was to hold workshops to gather their requirements and demonstrate how our analytics could address their challenges, ensuring that the solutions we provided were tailored to their specific needs.

I make it a priority to stay updated on analytics trends and technologies by subscribing to relevant newsletters, participating in online forums, and attending industry conferences. I also build projects around new technologies, allowing me to apply what I've learned in real-world scenarios.

One significant challenge I faced was dealing with disparate data sources that were not integrated. To overcome this, I developed a comprehensive data ingestion strategy that utilized APIs for real-time data capture and organized regular data cleaning sessions to address inconsistencies.

I prioritize tasks based on their urgency and impact, using project management tools to track progress. I also break larger projects into manageable milestones, ensuring that I maintain focus while adapting to any shifting priorities from the business teams.

I am motivated by the opportunity to turn raw data into meaningful insights that drive business decisions. The impact of my work on organizational success and being part of a progressive team that champions innovative solutions inspires me every day.