Senior Manager/Director, Data Engineering and Analytics

Maximus is a mission-driven consumer health company that provides men with content, community, and clinical support to optimize them in mind and body. Maximus has raised $15M from top Silicon Valley VCs such as Founders Fund and 8VC as well as leading angel investors/operators from companies like Bulletproof, Tinder, Coinbase, Daily Stoic, & Shopify.

Maximus is looking for a Senior Manager/Director, Data Engineering and Analytics to drive data-driven decision-making and processes optimization across the organization. They will be responsible for owning our KPI reporting and collaborating closely with the engineering team to ensure our analytics infrastructure is robust and scalable. This role requires a blend of strategic leadership and hands-on execution, making you a true player-coach. You will play a pivotal role in driving data-driven decision-making across the organization.

They will work directly with department heads in operations, finance, marketing, and product to establish priorities. They will manage analytics roadmap and capacity planning to ensure short and long-term analytics needs are met.

You will be the primary point of contact for data inquiries and will collaborate closely with teams to ensure their data needs are met. You will create and maintain dashboards, generate reports, and conduct detailed analyses to support strategic initiatives and day-to-day operations.

The ideal candidate has a strong analytical background, excellent problem-solving skills, and the ability to communicate complex data insights clearly and effectively. They should be proficient in using data analysis and visualization tools, have a proactive attitude, and possess excellent organizational and multitasking abilities. We’re looking for someone that is focused, goal-oriented, and takes full ownership of their work. Having a track record of setting and meeting deadlines providing error-free deliverables. Someone that is a strong investigator and curious to understand data.

Please note: Culture is key at Maximus, so we get to know candidates holistically through our questionnaire. You will receive the link upon submitting an application. Please fill it out as honestly and intuitively as possible since it is designed to detect exaggeration, which will cause your application to be automatically rejected. The questionnaire is optional to submit now with your application, but candidates that do so will get strongly prioritized. Note, we do not have access to individual questions’ answers/choices.

Responsibilities:

• Roadmapping and Planning: Manage analytics roadmap with cross-functional stakeholders. Oversee capacity planning and scope out future analytics needs and expansion. Clearly document deliverable requirements and host efficient working sessions as needed. Understand dependencies and expected timelines for each deliverable so that it can be prioritized and deadlines are upheld.

• Process & Standardization: Establish standards for documenting analytic requests, maintaining uniformity of metric definitions, and distributing reports to stakeholders with sufficient training

• Data Engineering Collaboration: Work closely with Data Engineering to ensure data needs are well-defined and prioritized based on analytics roadmap. Ensure all data collection methods meet legal regulations and standards. Collaborate on vendor and tool selection.

• Player-Coach: In addition to conducting analysis, provide coaching and mentorship to junior staff in data analysis and reporting.

• Data Collection and Analysis: Develop and maintain dashboards and reports to track key metrics and provide actionable insights. Gather, clean, and analyze data from various sources. 

• Data Interpretation and Presentation: Translate data findings into clear, actionable recommendations. All dashboards and reports developed contain sufficient information (data sources used, terminology, considerations/limitations), such that a stakeholder can leverage them self-service with confidence.

• Problem Solving: Identify trends, patterns, and anomalies in data. Investigate data anomalies, first ensuring data quality, then developing actionable insights that provide stakeholders a deepening understanding of business workings.

• Quality Assurance: Ensure data accuracy and integrity by conducting regular audits and validation checks. Develop and implement processes to improve data quality. See data accuracy and reliability as a requisite for all deliverables. 

Requirements:

• Experience: 5+ years of experience