Sr. Analytics Engineer - Data Platform

Mexico | Sept. 19, 2026

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Company: Koltin

Country: Mexico

Type: Remote

Employment: Full-time

Description:

About us

At Koltin, we are redesigning the way we care for our dads, moms, grandpas and grandmas, with the goal of helping them stay healthy, active and independent for as long as possible. We do this through our health memberships, which include:

• Clinical care focused on prevention
• Medical coverage for major expenses
• Wellness and community programs

We are the first company in Mexico that protects older adults, from 50 to 84 years old. We are growing rapidly across the country, with a clear purpose: to transform the health experience for Great People, making it more humane, accessible and dignified.

About the position

We are looking for a Mr. Analytics Engineer with a data platform inclination: the person who owns the *how*. You will design the data contracts and analytical modeling that make a number mean the same thing in any channel, and sustain the platform that makes it possible - ingestion, orchestration, quality and documentation.

It is a senior, hands-on and highly influential role: you set the technical bar, you mentor the team and you sit at the table where product, business and engineering make decisions.

Our ambition is clear: to go from answering well what is already asked, to anticipating the question, and from there to predictive models running in product. This role is the piece that makes that leap sustainable.

Responsibilities:

• Design and operate data contracts. A table per critical number, agreed upon with the team that owns the domain, versioned in dbt with commits, tests and catalog.
• Own analytical modeling. Layers, conventions and dimensional model in dbt, with documentation and CI that prevent debt from entering the door.
• Strengthen the data platform. Ingestion (Airbyte), orchestration (Dagster), quality and observability: that anomalies notify us before someone reports them.
• Enable real self-service. Semantic layer and models in Omni and Hex so that the questioner can answer alone, with Data as a guide and reviewer.
• Build high-trust deliverables. Dashboards and reports for board, external allies and regulatory reporting, where the margin of error is zero.
• Work close to the domain. Embedded with a product/business team, also contributing to transversal work on contracts, quality and standards.
• Raise the team's bar. Code review, pairing and technical mentoring; standards that are sustained without you in the room.
• Enable Data Science and ML. Reliable datasets, features and pipelines so that models reach production and their results return to the product.

Values:

• We measure work by impact, not by hours.
• Proactivity and productivity are equally important.⁠
• Speed ≠ speed (they look for “speed” with intentional direction).⁠⁠
• Consistency and consistency (aligning what we do with what Koltin and the GPs need).⁠⁠
• Honor the process (the process matters as much as the result).⁠

What we are looking for:

• 4+ years in data (analytics engineering, data engineering or BI with strong modeling), with at least 2 operating as a senior or technical reference.
• Advanced SQL and real analytical modeling criteria: dimensional, incremental loads, idempotency, history management.
• Data transformations in production. Experience bringing pipelines and data transformations to production with tools such as dbt or equivalent, defining good practices of structure, quality and testing, reusable components, documentation, CI/CD and orchestration of jobs/workflows.
• Pipeline development and automation. Experience building pipelines, automations and data processes with Python or equivalent technologies, applying good engineering practices such as Git, PRs/code review, testing and CI/CD.
• Experience with an orchestrator (Dagster, Airflow) and with managed ingestion (Airbyte, Fivetran or equivalent).
• PostgreSQL and/or an analytical warehouse (Redshift, Snowflake, BigQuery), with performance and cost in mind.
• Have built semantic layer or BI models for others to consult on their own (Omni, Looker, Hex, Metabase, Power BI, Other BI tools).
• Communication with business. Turn an ambiguous question into a metric definition that stands up to discussion, and defend it with data.
• Having mentored other engineers and left standards that survived.
• Native Spanish and professional English (documentation and tools in English).

Score points:

• GCP or AWS and infrastructure as code (Terraform, Pulumi or CDK).

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