Data Engineering Manager

About Carrot:

Carrot Fertility is the leading global fertility, family-building, and lifelong hormonal healthcare platform providing care for everyone, everywhere. Trusted by more than a thousand multinational employers, health plans, and health systems, Carrot's comprehensive clinical program delivers industry-leading cost savings for employers and award-winning experiences for millions of people worldwide. From maternity through menopause and pre-pregnancy through parenting, Carrot is dedicated to expanding access and improving outcomes. Carrot empowers members with compassionate, personalized, and inclusive support.

The Role: 

Carrot is seeking a Data Engineering Manager to join our Business Intelligence function as it scales to meet - and anticipate - the needs of the organization and its clients. As a Data Engineering Manager at Carrot, you will primarily be in a coaching/leadership role, but you should also be prepared to get in the weeds and work on technical projects given the small-but-mighty nature of the team. You will be tasked with defining and driving the development of the BI department’s data engineering function. Data Engineering Managers help the engineers on their team to develop, maintain, and support enterprise data solutions from ETL/ELT pipeline development to data warehouse administration and workflow orchestration. The reliability of these systems is essential for the accuracy and recency of data that is used by the organization to inform data-driven decision-making. This position requires a confident leader who is passionate about supporting career advancement and professional development for all data engineers, overseeing scalable data pipelines, and increasing efficiency and maturity in a fast-paced environment. The ideal candidate is a communicative, collaborative and thoughtful engineer who wants to have an impact on the organization by empowering the Data Engineering function of the BI team to be as effective and innovative as possible.

Core responsibilities for the role include:

• Management and mentorship of team members with different skill sets, levels of expertise, and technical areas of focus
• Clear, concise, and consistent upward communication to both technical and non-technical stakeholders
• Overseeing the design and execution of Business Intelligence data infrastructure, including but not limited to identifying opportunities for data acquisition, and exploring ways to enhance data quality and reliability
• Leading day-to-day operations of the team and the regular delivery of data engineering initiatives and projects
• Continuously seeking improved efficiencies, including potentially challenging existing processes and having difficult conversations to drive the team forward
• Leading strategic conversations with BI, engineering, and product leadership in order to gather, interpret, and document business requirements and translate them into engineering roadmap items
• Creating detailed documents using Atlassian tools to describe the solution and build architecture diagrams that adhere to standards to socialize the solution to leadership
• Overseeing and contributing to the development of software systems that enable advanced data analytics capabilities, for example, complex data visualization and advanced algorithms
• Facilitating team partnership with backend product engineers to optimize production database schemas with data use cases in mind
• Facilitating inter-departmental partnerships with stakeholder teams to improve their data infrastructure through automation support, data education, and strategic support
• Facilitating cross-team communication to minimize redundancies and keep teammates moving in step
• Mentoring and developing junior teammates through pairing, architecture discussions, PR reviews, and big-picture/strategic support

The Team:

The Business Intelligence team at Carrot is a highly cross-functional team that is central to Carrot’s long-term success.  The growing team is led by our Senior Director of Analytics and Business Intelligence and includes data engineers, data scientists, and business intelligence analysts.

Minimum Qualifications: 

• Minimum of 5-7 years in data engineering or related field, with advanced knowledge in SQL (4+ years of practical application) and Python development, including unit and integration testing (4+ years of practical application).
• Strong proficiency in cloud-based data technologies (e.g., Snowflake, AWS), database relational modeling, version control systems (Git preferred), and modern data ingestion/transformation tools (e.g., Fivetran, dbt).
• Demonstrated ability to build end-to-end ETL/ELT pipelines, orchestrat