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Koltin is redesigning elder and disabled care in Mexico, offering integrated health memberships combining preventive clinical care, major medical coverage, and wellness programs for adults aged 50–84. The company is growing rapidly across the country with a mission to make healthcare more human, accessible, and dignified.
You will own how data moves through the organization as a Senior Data Engineer on the Data Platform team. This is a hands-on senior role where you design, build, and operate the pipelines, ingestion, and orchestration that deliver complete, timely, and reliable data to the warehouse and downstream to product. You define platform architecture, set engineering standards (reliability, observability, CI/CD), and mentor the team. You work closely with Analytics Engineering, which builds data modeling and contracts on top of what you enable.
The platform's ambition is to evolve from answering known questions to anticipating them, and ultimately running predictive models in product. This role builds the scalable foundations that make that leap possible.
Key responsibilities:
- Design and operate data ingestion using managed connectors (Airbyte) and custom solutions (APIs, CDC) for clinical, commercial, and operational sources, with incremental, idempotent, and history-aware loads.
- Own orchestration: build Dagster workflows in Python with clear dependencies, retries, backfills, SLAs, and well-defined assets.
- Build production pipelines—batch and near-real-time, event-oriented where appropriate—that replace manual processes and scale without rewriting.
- Ensure data quality and observability through checks at each layer, alerts, lineage tracking, and monitoring so anomalies surface before users report them.
- Evolve cloud architecture on AWS (PostgreSQL/RDS, Lambda, S3, serverless) with performance, cost, and security in mind.
- Bring software engineering practices to data: Git, PRs, testing, CI/CD, reusable templates and components that boost team velocity and developer experience.
- Enable Analytics Engineering, Data Science, and ML teams with reliable raw data, training datasets, and pipelines that feed predictions back to product.
- Raise team standards through code review, pairing, and technical mentorship; establish standards that hold without you in the room.
The company values impact over hours, proactivity and productivity equally, intentional speed over rushing, consistency and alignment with organizational needs, and honoring process as much as results. The data platform is treated as a product with users in Analytics, Data Science, and business teams, measured by reliability and what it enables. Every request should leave capacity and knowledge behind through tests, monitoring, and documentation.
This is not a BI/dashboarding role, nor a query-support or ad-hoc extraction role. The heart of the work is ingestion, orchestration, and infrastructure.
Tech stack: PostgreSQL, dbt, Dagster, Airbyte, Python, AWS, Omni, Hex, Git/GitHub Actions, Slack, Notion, Claude.
Requirements:
- 5+ years in data engineering, with at least 2 years operating as a Senior Data Engineer or technical platform lead.
- Production Python for pipelines: ETL/ELT, automations, and data services with Git, PRs/code review, testing, and CI/CD.
- Real-world orchestration experience with Dagster or Airflow, ideally having deployed or migrated an orchestrator from scratch.
- Ingestion tools: managed platforms (Airbyte, Fivetran, or equivalent) and custom connectors against APIs; incremental loads, idempotence, and history handling.
- Advanced SQL and databases: PostgreSQL and/or an analytical warehouse (Redshift, Snowflake, BigQuery), optimizing for performance and cost.
- Data transformations with dbt or equivalent, understanding how ingestion and modeling contract with each other.
- Cloud experience, preferably AWS: RDS, Redshift, Lambda, S3, and serverless architectures (SAM or similar).
- Data reliability and observability: quality checks, monitoring, and alerting (Datadog, Grafana, or equivalent) in systems that cannot fail.
- Experience modernizing legacy or manual processes to cloud-native, scalable infrastructure.
- Ability to communicate with business and product teams, translating ambiguous needs into data solutions and explaining trade-offs.
- Track record mentoring other engineers and establishing standards that endure.
- Native Spanish and professional English (documentation and tools are in English).
Bonus points:
- Infrastructure as code (Terraform, Pulumi, CDK).
- Event-driven architectures and microservices; data APIs with FastAPI, REST, or GraphQL.
- Streaming or real-time processing for operational metrics.
- Big Data/OLAP (Vertica, Redshift, Snowflake) at scale.
- AWS, Dagster, or Snowflake certifications.
- Experience with clinical or health data and privacy/compliance requirements.
- Track record enabling Data Science/ML: training datasets, reusable features, predictions served to product.
- Startup growth context: high ambiguity, low bureaucracy, shifting priorities.
