Sr Data Engineer - Data Platform

Accountabilities: As a senior member of the Data Platform team, you will own how data moves through the organization, from ingestion and orchestration to reliable delivery for analytics and product use cases. Design and operate managed and custom data ingestion using tools such as Airbyte, APIs, and CDC for clinical, commercial, and operational sources. Build incremental, idempotent pipelines with appropriate historical data handling. Own orchestration workflows using Dagster and Python, including dependencies, retries, backfills, SLAs, and well-defined assets. Build production-grade batch and near-real-time pipelines, including event-driven architectures where appropriate. Replace manual processes with scalable data systems that can evolve without requiring repeated rewrites. Establish data quality checks, monitoring, alerts, and lineage across data layers so anomalies are detected proactively. Evolve cloud architecture using AWS services such as PostgreSQL/RDS, Lambda, S3, Redshift, and serverless technologies while balancing performance, cost, and security. Introduce and maintain engineering best practices across the data platform, including Git, pull requests, testing, CI/CD, templates, and reusable components. Enable Analytics Engineering, Data Science, and ML teams with reliable raw data, dbt-compatible foundations, training datasets, reusable features, and pipelines that return predictions to products. Participate in code reviews, pairing, and technical mentorship while establishing standards that can scale across the team. Collaborate with product and business stakeholders to translate ambiguous needs into practical data solutions and clearly communicate technical trade-offs. Contribute to a product-oriented platform culture where reliability, capability, and the value enabled for internal users are key measures of success. Requirements The role requires strong production data engineering experience, technical ownership, and the ability to operate effectively in a growing environment where priorities can evolve quickly. 5+ years of experience in data engineering, including at least 2 years operating as a Senior Data Engineer or technical platform reference. Strong Python experience for production pipelines, ETL/ELT, automation, and data services. Experience applying Git, pull requests, code reviews, testing, and CI/CD practices to data engineering. Hands-on experience with Dagster or Airflow, ideally including deploying or migrating an orchestration platform from the ground up. Experience with managed ingestion tools such as Airbyte, Fivetran, or equivalent, as well as custom API connectors. Strong understanding of incremental processing, idempotency, and historical data management. Advanced SQL skills and experience with PostgreSQL and/or analytical warehouses such as Redshift, Snowflake, or BigQuery. Experience with dbt or an equivalent transformation framework and a solid understanding of the relationship between ingestion and data modeling. Cloud experience, preferably with AWS services including RDS, Redshift, Lambda, S3, and serverless architectures such as SAM or similar. Experience implementing data quality checks, monitoring, and alerting using tools such as Datadog, Grafana, or equivalent. Demonstrated ability to modernize legacy or manual processes into scalable, cloud-native infrastructure. Strong communication skills and the ability to translate ambiguous business or product needs into effective data solutions. Previous experience mentoring other engineers and establishing technical standards that remain effective over time. Native-level Spanish and professional English for documentation, technical tools, and collaboration. Experience with infrastructure as code, such as Terraform, Pulumi, or CDK, is a plus. Familiarity with event-driven architectures, microservices, data APIs, streaming, or real-time processing is advantageous. Experience with large-scale OLAP or Big Data environments such as Vertica, Redshift, or Snowflake is a plus. AWS, Dagster, or Snowflake certifications are welcome. Experience with healthcare or clinical data and related privacy and compliance requirements is advantageous. Experience enabling Data Science or ML teams through training datasets, reusable features, or production prediction pipelines is a plus. Experience working in a fast-growing startup environment with high ownership and limited bureaucracy is valuable. Benefits Fully remote position in Mexico. Full-time employment. Access to professional development resources, including courses, workshops, books, and other learning tools. Unlimited private medical support by video for you and up to 3 family members, including general medical, nutrition, and psychological consultations. Access to physical wellness benefits through TotalPass. 9 additional personal leave days per year beyond statutory requirements. Major medical expense insurance. Equipment required to perform the role. Monthly support of MXN 700 in addition to salary. One-time support of MXN 2,500 after the probationary period. Opportunity to work with a modern data stack including PostgreSQL, dbt, Dagster, Airbyte, Python, AWS, and GitHub Actions. High level of ownership, autonomy, and flexibility in how technical solutions are designed and implemented. Collaborative environment with close interaction across Data, Analytics Engineering, Data Science, ML, Product, and business teams.