About us
At Koltin, we’re redesigning the way we care for our moms, dads, and grandparents to help them remain healthy and independent for as long as possible.
How do we do it? Through our health memberships, which provide personalized, preventive care to support our members’ long-term well-being.
We are the first company in Mexico offering health memberships that include major medical insurance coverage for older adults-up to 84 years old-backed by BBVA Seguros Salud.
About the role
The Senior Data Analyst at Koltin plays a critical role in turning complex operations into clear visibility, insights, and decisions.
This role is responsible for building and maintaining operational visibility across two key areas:
- Clinical Operations: clinical funnels, medical capacity, provider utilization, demand.
- Insurance Operations: applications, payments, renewals, collections, and claims-related flows.
You will act as a bridge between data and the business, translating operational complexity into dashboards, analyses, projections, and actionable recommendations that drive impact.
Ideal backgrounds:
Actuarial Science, Economics, Engineering (Industrial, Systems, etc.), Mathematics, Physics, or related quantitative fields.
You’re a good fit if you:
- Identify with our Values: Ownership, Collaboration, Excellence, Data-driven, Curiosity.
- Take full ownership of operational visibility: you don’t just answer questions, you proactively surface the right ones.
- Are deeply data-driven: you ground decisions in metrics, trends, and evidence, while understanding the limitations and assumptions behind the data.
- Enjoy translating messy, real-world operations into clean funnels, KPIs, and dashboards.
- Are curious by default: you ask “why” repeatedly, challenge assumptions, and continuously deepen your understanding of clinical and insurance operations.
- Communicate clearly and confidently with both technical and non-technical stakeholders, adapting your message to your audience.
- Collaborate closely with Data Engineering, Engineering, Ops, and Medical teams to ensure data accuracy, trust, and usability.
- Are comfortable working in a fast-moving startup environment with evolving priorities, tools, and processes.
- Use AI-powered tools to improve productivity and insight generation (analysis, summarization, exploratory work), while retaining full ownership of conclusions and recommendations.
Had experience with:
- Strong SQL skills and experience with relational databases.
- Python for data analysis (pandas, matplotlib/seaborn or similar).
- Building and maintaining operational dashboards in BI tools (e.g. Omni, Metabase, Looker).
- Defining and owning KPIs, funnels, and operational metrics.
- Exploratory analysis to identify trends, anomalies, and opportunities.
- Working with messy, real-world operational data.
- Collaborating with Data Engineering and non-technical stakeholders.
- Communicating insights clearly through charts, dashboards, and written summaries.
- (Nice to have) Experience in healthcare, insurance, fintech, or complex operational domains.
Key Outcomes:
- Clinical Ops and Insurance Ops have clear, trusted, and up-to-date operational visibility through dashboards and core metrics.
- Operational funnels (clinical capacity, payments, applications, renewals, collections) are well-defined, maintained, and actively used for decision-making.
- Bottlenecks, anomalies, and risks are identified early, enabling teams to act before issues escalate.
- Demand and capacity projections inform planning and resourcing decisions for clinical operations.
- Stakeholders use data to prioritize actions, not just to report outcomes.
- Analysis and recommendations drive measurable improvements in operational efficiency and key business metrics.
- Data quality and metric definitions improve over time through collaboration with Data Engineering.
- The organization increasingly trusts and relies on data as a core input for decisions.
How we hire:
Note: There might be a couple more steps in between if we see the need.
- Initial conversation with a recruiter (15–20 min)
An intro call to get to know you, understand your background, and share context about the role, the team, and Koltin.
- Asynchronous technical exercise (time-boxed)
A real-world–inspired scenario to understand how you:
- Structure problems
- Analyze data
- Communicate insights and recommendations clearly
We focus on reasoning, clarity, and trade-offs—not just the final answer.
- Discussion with our Principal, Data Strategy (60 min)
A deeper conversation on:
- Technical and analytical depth
- Decision-making and prioritization
- Data quality, metrics, and operational thinking
- Conversation with our VP of Product Engineering (30 min)
This conversation focuses primarily on cultural fit and how you operate day to day, including:
- Communication skills: clarity, structure, and ability to adapt your message to different audiences
- Problem orientation: how you frame ambiguous problems and turn them into actionable analyses
- Ownership and accountability: how you take responsibility for outcomes, not just analyses
- Collaboration style in a cross-functional environment
- Final conversation with one of our founders (30 min)
We close the loop with a discussion around vision, culture, and long-term fit, and how you see yourself growing with Koltin.
Perks & Benefits:
- 🎓Access to professional development tools and resources (courses, books, workshops, etc.)
- 💊Unlimited virtual medical assistance (general practitioner, nutritionist, psychologist) for you and 3 family members
- 🏈 Access to physical wellness tools (TotalPass)
- 🌴 9 extra vacation days per year in addition to the legal minimum
- 👓 Private Major Medical Insurance
- 💻 All the equipment you need to do your best work
- 💵 $500 MXN monthly support for home office expenses
- 💎 One-time $2,000 MXN support to set up your home workspace
Being aware that some groups don’t apply even if they are qualified, this is a reminder to apply even if you think that you don’t tick all the boxes!
*Advanced Spanish proficiency is strongly preferred for this role.
