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Koltin is redesigning elder and disabled care in Mexico, offering health memberships that integrate clinical care focused on prevention, major medical expense coverage, and wellness/community programs. The company serves adults aged 50-84 and is growing rapidly across the country.
You will own the data platform as a Senior Analytics Engineer with a platform-first mindset. This is a hands-on, high-influence senior role where you set technical standards, mentor the team, and sit at the table where product, business, and engineering make decisions.
Key responsibilities:
- Design and operate data contracts: one table per critical metric, versioned in dbt with commits, tests, and catalog.
- Own analytical modeling: layers, conventions, and dimensional models in dbt with documentation and CI to prevent technical debt.
- Strengthen the data platform: manage ingestion (Airbyte), orchestration (Dagster), quality, and observability so anomalies alert before users report them.
- Enable real self-service: semantic layers and models in Omni and Hex so stakeholders can answer their own questions with Data as guide and reviewer.
- Build high-confidence deliverables: dashboards and reports for board, external partners, and regulatory reporting where error margin is zero.
- Work embedded with product/business teams, contributing to cross-functional work on contracts, quality, and standards.
- Raise team standards through code review, pairing, and mentorship; establish standards that survive without you in the room.
- Enable Data Science and ML: provide reliable datasets, features, and pipelines so models reach production and results feed back to the product.
The company values impact over hours, balances proactivity with productivity, emphasizes intentional speed over velocity, maintains consistency aligned with company needs, and honors process as much as results. Work is measured by impact, not hours. The team operates with low hierarchy, real ownership, and space to decide how things are built.
Tech stack: PostgreSQL, dbt, Dagster, Airbyte, Python, Omni, Hex, AWS, Git/GitHub Actions, Slack, Notion, Claude.
REQUIREMENTS:
- 4+ years in data (analytics engineering, data engineering, or BI with strong modeling), with at least 2 years operating as senior or technical reference.
- Advanced SQL and real analytical modeling judgment: dimensional modeling, incremental loads, idempotence, history management.
- Production data transformations: experience shipping pipelines and transformations with dbt or equivalents, defining best practices for structure, quality, testing, reusable components, documentation, CI/CD, and job/workflow orchestration.
- Pipeline development and automation: building pipelines, automations, and data processes with Python or equivalent technologies, applying engineering best practices (Git, PRs/code review, testing, CI/CD).
- Experience with an orchestrator (Dagster, Airflow) and managed ingestion (Airbyte, Fivetran, or equivalent).
- PostgreSQL and/or an analytical warehouse (Redshift, Snowflake, BigQuery) with performance and cost awareness.
- Built semantic layers or BI models for self-service querying (Omni, Looker, Hex, Metabase, Power BI, or similar).
- Business communication: convert ambiguous questions into defensible metric definitions and argue them with data.
- Mentored other engineers and left standards that survived.
- Native Spanish and professional English (documentation and tools are in English).
BONUS:
- GCP or AWS and infrastructure-as-code (Terraform, Pulumi, CDK).
- REST API, GraphQL, and microservices architectures.
- Clinical or health data experience with privacy and compliance requirements.
- OLAP architecture knowledge (MOLAP, ROLAP).
- Enabled Data Science/ML work: training datasets, reusable features, predictions served to product.
- Startup growth context: high ambiguity, low bureaucracy, shifting priorities.
