Senior Data Analyst

About Existence

Existence is a Time Intelligence Platform that changes how people understand and create their lives through time.

Existence turns the time you create into the data of the Real You, making your patterns, energy, habits, experiences, and the alignment between intention and reality visible and measurable. Through structured time data, behavioral intelligence, personalized analytics, and AI, Existence helps people understand themselves more deeply, design their time with greater intention, and continuously realize their potential.

At the center of the platform is a uniquely rich and expanding dataset of human behavior and lived experience. As that dataset grows, so does our ability to generate meaningful intelligence, from how people spend and experience their time to an AI-powered Time Agent that understands each user through the data of their actual life.

The Role

We're hiring a Senior Data Analyst to be the central analytical resource for Existence. You'll partner with Product, Marketing, Finance, and the executive team so that leaders have accurate, actionable, and timely information to make better decisions.

This is a hands-on role. You'll own the analytics layer of the business: defining the metrics that matter, building the dashboards and reports people rely on, digging into user behavior, designing and reading out experiments, and shaping what data we collect in the first place. You'll work closely with our Data Engineer, who owns the warehouse and pipelines that feed your work.

Critically, we want someone who is at the forefront of AI-enabled analytics and can integrate the latest AI technologies into our data stack, reporting systems, analytical workflows, and business intelligence capabilities.

How This Role Works with Data Engineering

Existence has a dedicated Data Engineer who owns the data platform. This role owns what the business does with it. The two roles work as a pair.

OWNS

▸ Data Engineer: Warehouse (Snowflake), pipelines and orchestration (Dagster), source integrations, data models and transformations

▸ Senior Data Analyst (you): Metric definitions, dashboards and reports, analysis, experimentation design and readout, executive reporting

INSTRUMENTATION

▸ Data Engineer: Builds and maintains the event pipelines

▸ Senior Data Analyst (you): Specifies what to track and why, defines event and property requirements, validates the data once it lands

DATA QUALITY

▸ Data Engineer: Pipeline reliability, freshness, schema integrity

▸ Senior Data Analyst (you): Metric correctness, definitions, and documentation

OUTPUT

▸ Data Engineer: Clean, modeled, trusted data

▸ Senior Data Analyst (you): Intelligence the business acts on

What You'll Own

• KPI architecture. Work with department heads to consolidate, standardize, and monitor the metrics that matter across the consumer software funnel: acquisition, activation, engagement, retention, conversion, monetization, LTV, CAC, churn, and cohort performance.
• Company dashboards and reporting. Build and maintain the dashboards and recurring reports used across Product, Marketing, Finance, and leadership, including our daily product-metrics report and the intake process for new metric definitions.
• Product analytics. Analyze user behavior, feature adoption, engagement, retention, cohorts, and funnels so Product leadership has a clear picture of how the product is performing.
• Consumer-level analytics: Build user-level reporting that enables segmentation, cohort and longitudinal analysis, and connects behavioral data with user feedback, surveys, and research to better understand patterns in engagement, retention, and the overall user experience.
• Growth and marketing analytics. Partner with Marketing to measure acquisition efficiency, attribution, top-of-funnel channel performance, conversion, lifecycle performance, and CAC/LTV/ROAS dynamics.
• Experimentation. Design and run A/B tests and other experiments, apply the right statistical methods, and read out results so teams build on what they've learned.
• Executive reporting. Deliver concise, reliable reporting to the CEO, COO, CPO, and CMO that turns complex data into clear business intelligence.
• Instrumentation and data requirements. Define what we need to collect and how it should be modeled, then partner with Data Engineering to get it into the warehouse and validate it.
• Data integrity. Set the standards for metric definitions, documentation, and analytical rigor so every team works from the same numbers.
• AI in the workflow. Use AI tools to speed up analysis, reporting, and how the company asks questions of its data, and bring in new ones as they prove useful.

What We're Looking For

Must have

• 5+ years in analytics, product analytics, or a closely related quantitative role, including meaningful time inside a consumer software or consumer technology business.
• Strong SQL and comfort working with large behavioral datasets in a modern warehouse (we use Snowflake).
• Fluency in consumer software KPIs across Product, Growth/Marketing, and Finance, and a track record of defining and standardizing them.
• Demonstrated experience building dashboards, recurring reports, and executive-level business intelligence.
• Solid grounding in experimentation: designing A/B tests, choosing appropriate statistical methods, and communicating results honestly.
• Experience with cohort analysis, segmentation, funnel analysis, and retention modeling.
• Ability to explain quantitative findings clearly to both technical and non-technical executives.
• Comfortable in a fast-moving, early-stage environment where the data function is still being built.
• Can be trusted with sensitive user data and held to the highest security and privacy standards.
• Must have a a clear sense of the difference between reporting and insight: you don't stop at 'here's what the numbers show,' you form a point of view and make a recommendation

Nice to have

• Python for analysis.
• Experience with dashboards-as-code tools (we use Evidence.dev) and product analytics platforms (we use Mixpanel).
• Familiarity with orchestration and transformation tooling (we use Dagster) well enough to collaborate with Data Engineering.
• Already using LLMs and AI tools as a regular part of your analysis and reporting workflow.
• Exposure to forecasting or predictive modeling.

What Great Looks Like

Within your first year, every major function at Existence operates from consistent, trusted data. Leadership has clear visibility into company performance, experimentation is rigorous and measurable, and AI has materially increased the speed at which we can understand our business.

Your job is to make sure every leader at Existence is deciding from the same, correct numbers, and to be the person who notices when they aren't.

Compensation is competitive and will be based on experience, skills, and qualifications.