• Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space • Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence. • Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models. • Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs. • Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability. • Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability. • Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability. • Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability. • Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation. • Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows.

• Data Engineering & Architecture — Own the foundational marketing data layer. You will design, build, and maintain the data pipelines that transform raw marketing sources into optimized dimensional models (utilizing Medallion architecture), delivering clean, report-ready tables for downstream business intelligence and analytics; • Predictive Analysis — Develop and apply statistical and predictive models to forecast key marketing funnel metrics, identify areas for improvement, and inform strategic decision-making; • Marketing Attribution — Support the design and implementation of marketing attribution models to accurately measure channel and campaign contribution; • Data Quality & Governance — Ensure data reliability through monitoring, validation, cleansing, and adherence to data governance policies; • Documentation & Versioning — Maintain thorough documentation of the marketing data ecosystem and version data projects in GitHub; • Semantic Layer for AI — Maintain and extend BI data models (LookML and/or dbt preferred) enriched with metadata, business context, and structured definitions that make them consumable by AI agents and natural language analytics interfaces; • Reusable AI Skills & Workflows — Build reusable AI skills, tools, and workflows that allow analysts and stakeholders to interact with governed data through natural language interfaces; • AI Quality Assurance — Run structured test cycles to validate AI agent output accuracy, identify regressions, and maintain quality standards as models and data evolve; • Data Standards for Responsible AI — Contribute to metadata standards and responsible AI guidelines that govern how marketing data is surfaced through automated and AI-assisted analytics.

Who We AreInato is a Tech for Good company striving to bring clinical research to each and every patient, regardless of who they are or where they live. To do this, we are building the world's first clinical trial platform to create greater visibility, access, and engagement across a more diverse population of doctors and their patients. Drug development is a challenging, intellectually complex, and rewarding endeavor: we enable global pharmaceutical companies to confidently partner with community-based researchers to increase patient access to the latest medical innovations. Our AI-powered platform currently offers clinical trials from leading companies to over 5,500 sites across the globe and we are well poised for growth in 2026. We are a growing team of passionate pharmaceutical experts, software and AI engineers, professional services members, and many more—all bringing their unique perspectives to solve the challenges facing clinical research. Inato is the recent recipient of Fast Company’s Most Innovative Companies of 2024, Fierce Healthcare’s Fierce 15, and Built In's Best Places to Work 2025. The Role We're hiring a Senior Analytics Engineer, Product to own how data moves from Inato's platform into the hands of the people who depend on it — our product squads, CS and Marketing teams, sponsors, and sites. You'll enable trusted self-service at scale, prove the value of our new product offerings faster, and ship data products directly into the user experience. You'll report to Alexandre Halley (Data Director) and partner daily with Product squads (PMs, engineers, designers), with regular touchpoints into CS, Marketing, and Sponsor / Site Ops. Our stack includes Segment, Airbyte, Dagster, BigQuery, dbt, Hex, FullStory, and more. What you'll own - Trusted self-service at scale. Own metric definitions, build the semantic layer powering our AI-driven self-service in Hex, and govern what gets exposed so non-data teams can answer their own questions confidently. - Faster value validation for new offerings. Partner with squads to translate vague operational pains (a CS workflow, a sponsor enablement gap, a prototype idea) into working data assets in days — from real-data prototype to production-grade pipeline. - Data products in front of users. Own the tables consumed by the product itself, and the user-facing dashboards (embeds, custom interfaces) that sponsors, sites, and internal teams rely on day to day. - Using AI to scale yourself and the team. Automate the parts of data work that don't need a human in the loop so the team can keep pace as new offerings multiply and Inato grows. The data team is pioneering AI-enablement internally, and this role is at the front of that. What success looks like - By 3 months: You own at least one new-offering data pipeline end-to-end. Corresponding data points are available for self-service, and new metric definitions are live. You've automated at least one internal process using AI. - By 6 months: At least three user-facing dashboards or data streams are live. Requirements Must-have - 4+ years as an Analytics Engineer, or in a hybrid data engineer/analyst role with strong analytics engineering ownership. - Strong dbt + warehouse modeling skills (we use BigQuery). - Track record of owning data products end-to-end: definition → ship → measurable adoption. - A genuine product mindset — you start from the user's problem, not from the SQL. - Excellent written and verbal communication with non-data stakeholders. - Comfort navigating ambiguity — you can turn a vague operational pain into a working data asset without a fully-specified ticket. Nice-to-have - Fluency with AI-assisted tooling (Claude Code, Claude Cowork, Hex AI) and a habit of using it to ship faster. - Experience building production-grade company dashboards. - Domain exposure to healthcare, clinical trials, or other regulated industries. Working style You're impact-driven and pragmatic — you instinctively reach for the shortest path to the user's need. You're systematic: you'd rather improve the system than re-fix the same symptom. You're satisfied shipping the 80% solution at 20% of the effort, then iterating on it. You're equally comfortable in a stakeholder conversation, a dbt PR, and a quick prototype. You'll thrive here if your career arc looks something like: started as an analyst or PM, moved up the stack toward analytics engineering or data product, have lived in start-ups or scale-ups where you owned a meaningful slice of the data value chain end-to-end, and have shipped complex projects with measurable end-user value. Location and Compensation - France-based, remote-first. Office available in Grands Boulevards, Paris if you want to use it. - Base salary range: 65,000€ – 80,000€, plus equity. No individual variable bonus. Why this role, why now Inato is in a phase of rapid growth driven by new product offerings, and the data team is one of the functions directly responsible for proving and scaling that value. You'll have a direct line to the VP Product, real ownership from day one, and a small senior team around you. Inato is leaning hard into AI-enablement, and the data team is pioneering it internally — there's a clear path to Lead or Staff Analytics engineer, or Data Product Manager within 2–3 years as our data-as-a-product surface grows. Perks - Remote-first philosophy & flexible hours - Amazing office in Grands Boulevards, Paris where you can meet with colleagues if this is important for you - Top-of-the-line equipments - Compensatory time (RTT) - Health insurance (Benefiz, 100% paid by Inato) - Meal vouchers (Swile) - Contribution to healthy activities (Wellpass, ex-Gymlib) - Free books & learning material

• Lead, coach, and develop a team of Lead and Senior Analytics Engineers - driving career growth, performance and a high-trust team culture. • Stay deeply hands-on: write and review SQL/dbt code, drive technical design, and uphold a high bar for craftsmanship, testing and documentation across our Snowflake + dbt stack. • Own the roadmap for your team’s portion of the analytics platform - prioritizing work that drives the most business impact and balancing delivery, quality and technical debt. • Drive Forward’s data centralization vision by partnering with Business Intelligence and wider Analytics group to design and deliver scalable data models, marts, and a reliable semantic layer that serves as the company’s single source of truth. • Lead the build-out of our semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products. • Translate ambiguous business problems from leadership and cross-functional stakeholders into well-scoped technical solutions, communicating tradeoffs clearly to both technical and non-technical audiences. • Champion data quality, observability, and governance - including testing, lineage, cataloging, and incident response - so stakeholders can trust the numbers. • Drive technical excellence in our dbt project: model architecture, performance tuning, incremental strategies and CI/CD practices that scale as data volume and team size grow. • Help shape our hiring bar - recruit, interview and onboard exceptional Analytics Engineers as the team continues to grow.