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 Sr. Analytics Engineer at Koltin sits at the crossroads between business, data, and analytical rigor. You’ll turn clinical records, lab results, diagnoses, and claims data into clear risk insights that inform prevention strategy, operational decisions, and financial planning.

What makes this role distinct is the combination of three things we rarely find in one person: the ability to translate seamlessly between what the business needs and what the data can actually answer; the technical foundation to navigate our data warehouse, write complex queries, and understand how our data is structured; and the analytical depth to go beyond dashboards—building cohort analyses, risk segmentations, and statistical assessments that hold up to scrutiny.

You’ll work on questions like: Which members show early warning signs before a major health event? Are claims patterns in this cohort explained by age and tenure, or is there a structural driver we’re missing? Is our prevention program reaching the members who need it most? How do we translate a statistical finding into a recommendation the clinical team can actually act on?

You will collaborate closely with our clinical team, data engineers, actuaries, and business leadership—serving as the person who bridges technical depth with business context.

Ideal backgrounds: Actuarial Science, Economics, Engineering (Industrial, Systems, or related), Mathematics, Statistics, or quantitative fields with at least 4 years of relevant experience. The ideal candidate has built the habit of questioning their own numbers and communicating uncertainty with precision.

You’re a good fit if you:

Identify with our Values: Ownership, Collaboration, Excellence, Data-driven, Curiosity.
Are a natural translator between worlds: you can take a vague business question, turn it into a precise analytical problem, execute the analysis, and then explain the result to a medical director without losing them.
Are statistically rigorous without being a purist: you know when a result is too good to be true, you report confidence intervals, and you design analyses that control for the obvious confounders—without over-engineering.
Know your way around a data warehouse: you can follow a schema, trace a metric back to its source tables, and write the SQL to build the dataset you need from scratch.
Think in populations: you care about how cohorts of members evolve over time, not just what the aggregate number says this month.
Are proactive in surfacing the right questions: you don’t wait to be asked—you identify what’s missing and bring it to the table.
Communicate clearly with both technical and non-technical stakeholders, adapting your depth and framing to the audience.
Are collaborative: you work well with doctors, data engineers, and actuaries, especially when their mental models differ from yours.
Are comfortable in a startup environment where data definitions evolve, tools change, and not everything is documented.
Communicate well in Spanish & English.

Had experience with:

Minimum 4 years of relevant experience in analytics, risk, or data-heavy roles.
SQL at an advanced level: multi-table joins, window functions, subqueries, CTEs, and the ability to navigate and query a DWH without hand-holding.
Statistical analysis in Python or R: regression models, segmentation, A/B testing, cohort comparisons, correlation and significance testing.
Risk or claims analysis: understanding frequency and severity patterns, cohort differences, cost drivers, and how to isolate a signal from noise.
Building clear, decision-grade analyses and communicating them through well-structured tables, charts, and written summaries.
Working with messy, real-world operational data where not every join is clean and not every field is documented.
Translating analytical findings into actionable recommendations for non-technical stakeholders.
(Nice to have) Experience with health, insurance, or clinical datasets.
(Nice to have) Familiarity with actuarial concepts (loss ratios, pricing basics, MLR)—conceptually comfortable, not exam-level.
(Nice to have) Exposure to causal inference methods (difference-in-differences, matching, propensity score approaches) for evaluating program effectiveness.
(Nice to have) Experience working in a data warehouse environment (e.g., BigQuery, Snowflake, Redshift) and understanding of data modeling basics.
(Nice to have) Experience in Mexico or Latin America healthcare or insurance markets.

Key Outcomes:

Business questions are answered with statistical rigor: analyses are designed to isolate signals, control for confounders, and report uncertainty—not just produce a number.
Risk segmentation is backed by evidence: cohort definitions and risk tiers are grounded in claims patterns, member behavior, and analytical findings—not just business rules.
Claims patterns are understood at depth: frequency and severity differences by age band, condition, and risk profile are documented and communicated to clinical and financial leadership.
Prevention program impact is measured: analyses that track whether interventions are reaching the right members and producing measurable changes in claims behavior.
Data warehouse is navigated independently: the Sr. Analytics Engineer can build its own datasets from source tables without depending on data engineering for every query.
Insights reach the right people in the right form: clinical, actuarial, and business stakeholders receive findings that are clear, actionable, and calibrated to their decision context.
Methodologies are documented so analyses can be reproduced, audited, and built upon over time.

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

💵 $700 MXN monthly support for home office expenses

💎 One-time $2,500 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!