Senior Data Analytics Engineer

The Senior Data Analytics Engineer at Fullscript will be responsible for defining, building, and deploying the Enterprise Data Warehouse that meets the reporting and analytical self-service needs of the organization. This role involves writing data transformation code, collaborating cross-functionally, mentoring junior team members, and continuously optimizing data pipelines for performance and scalability. With your skills, you will ensure data quality and help Fullscript leverage data effectively to drive impactful decisions.

Candidates for the Senior Data Analytics Engineer position at Fullscript should have at least 5 years of experience in Data Analytics or Data Engineering, particularly with ETL/ELT processes and data insights development. Strong proficiency in SQL and Python, familiarity with dbt, as well as hands-on experience with cloud data warehouses such as Snowflake or BigQuery, is essential. Additionally, knowledge of data modeling, schema design, and continuous integration practices will be highly regarded.

As a Senior Data Analytics Engineer at Fullscript, you will work with various tools and technologies that include SQL for querying and managing data, Python for data transformation, and dbt for building data transformation pipelines. You will also be involved with cloud data warehouses like Snowflake and BigQuery. Adopting advanced data engineering techniques and orchestrating workflows with tools like Airflow or Argo will be part of your regular tasks to ensure efficient data handling and analysis.

Fullscript is committed to fostering professional growth and learning for its Senior Data Analytics Engineers by providing a training budget, company-wide learning initiatives, and mentorship opportunities within the team. The culture encourages continuous learning and exploration of new technologies and methodologies, helping engineers stay current with industry advancements while contributing to impactful projects.

Fullscript champions a flexible work environment, allowing Senior Data Analytics Engineers to choose how they work best—whether that’s from home, in-office, or a combination of both. Our 'Wherever You Work Well' philosophy embraces remote work while providing opportunities for in-person team connections, ensuring that team members can maintain a work-life balance while contributing to our collaborative culture.

In answering this question, focus on specific projects where you've utilized SQL to derive insights or optimize data management. Discuss how your proficiency helped streamline reporting processes and supported decision-making, showcasing your understanding of its importance in the context of Fullscript's mission.

A strong answer would detail your experience working with cloud data warehouses such as Snowflake or BigQuery. Emphasize your processes for ensuring data quality, including testing, validation practices, and how you monitor data integrity over time, tailoring your response to how these practices align with Fullscript's value of making data accessible.

In your response, explain your methodology for constructing data transformation code, whether it be through defined workflows, version control practices, or using tools like dbt. Detail how you apply CI/CD principles to foster a smooth deployment cycle, which is crucial for a Senior Data Analytics Engineer at Fullscript.

Share a specific example of a challenging data analytics project, highlighting the obstacles you faced—be it technical difficulties or team dynamics. Discuss how you overcame these issues, the results achieved, and how this experience prepares you for the challenges you might encounter at Fullscript.

Emphasize the importance you place on thorough documentation as a component of your work. Discuss your methods for creating and maintaining clear definitions of data models and your approach to keeping documentation updated, ensuring that it remains a valuable resource for your team and stakeholders at Fullscript.

Discuss data engineering best practices that you believe are critical—such as version control, testing, scalable architecture, and CI/CD methodologies. Link these practices to how they can enhance Fullscript's data team performance and effectiveness in driving business decisions.

Describe your mentoring philosophy—how you would provide guidance, share knowledge, and foster a learning environment. Mention the importance of hands-on experience and what specific strategies you would use to help junior engineers grow successful data analytics skills in their roles at Fullscript.

Share your passion for data analytics and engineering, explaining what most excites you about working in this space, particularly in a healthcare environment. This could be the potential for impactful decision-making and improving patient outcomes, aligning with Fullscript's mission.

Convey your proactive approach to continuous learning. Mention resources such as online courses, industry conferences, communities, or reading articles that help you stay current with advancements in data engineering, and how these insights could benefit the team at Fullscript.

Discuss the opportunities data analytics presents in enhancing patient care, such as optimizing treatment plans based on holistic patient data, improving operational efficiencies, or providing insights into patient outcomes. Relate these points to how you see yourself contributing to Fullscript's mission through your role.