Sr Data Engineer - Data Platform

This is a senior, hands-on data engineering role responsible for building the infrastructure that keeps critical data flowing reliably across the organization.
You will design and operate ingestion pipelines, orchestration workflows, and cloud infrastructure that make data complete, timely, and trustworthy.
The role combines platform architecture, production engineering, observability, and developer experience with a strong focus on scalability and reliability.
You will work closely with Analytics Engineering, Data Science, and Machine Learning teams to provide the foundations they need to build models and data products.
A key part of the mission is modernizing manual and legacy processes into robust cloud-native systems that can support growing data volumes and use cases.
You will also establish engineering standards around testing, CI/CD, monitoring, and reusable components while mentoring other engineers.
This opportunity is ideal for a senior data engineer who enjoys ownership, technical decision-making, and building platforms that enable predictive and data-driven products.

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.