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ABOUT THE ROLE

We are looking for a Senior Data Engineer to operate and improve a Snowflake-based enterprise data platform in a regulated healthcare environment. WHAT YOU WILL DO Provide senior technical ownership for the Data Platform service tower during the Lat Am coverage window, including day-to-day operations, complex troubleshooting, and L2/L3 escalation. Operate and improve Snowflake production and non-production environments, including warehouse configuration and sizing, performance and consumption monitoring, object lifecycle, environment hygiene, and support for production changes. Administer data access within established controls, including users, roles, service accounts, secrets, and credential rotation, while maintaining least-privilege and audit-ready practices. Operate and improve data ingestion across Fivetran, HVR where applicable, and custom pipelines, including connector configuration, scheduling, source onboarding, schema-change coordination, failure recovery, backfills, and dependency management. Design, build, and maintain reliable pipelines and dbt models across RAW, CURATED, and CONSUMPTION layers, with appropriate testing, documentation, lineage, version control, and CI/CD practices. Support AWS S3 data-lake operations, including raw and landing-zone workflows, lifecycle and retention controls, access patterns, logging, ingestion failures,



and coordination with downstream Snowflake workloads. Support Argo Workflows and Kubernetes-hosted data workloads in close coordination with the Cloud / Dev Ops team, including scheduling, troubleshooting, deployment, recovery, and capacity dependencies. Define and improve data quality and observability standards, including freshness, zero-row, row-count growth, null, duplicate, schema-drift, and referential-integrity checks. Expand end-to-end monitoring and lineage using tools such as SYNQ, dbt, Snowflake audit data, Splunk, and the agreed alerting stack, linking actionable alerts to evidence and runbooks. Lead or support major data incidents, root-cause analysis, post-incident reviews, and preventive actions across ingestion, orchestration, Snowflake, and downstream Tableau dependencies. Support Tableau Cloud operations where upstream data, connectivity, permissions, extracts, or refresh failures require Data Platform investigation. Identify and deliver standardization, automation, reliability, performance, and cost improvements, including migration of suitable legacy or custom extraction patterns toward agreed golden paths. Execute work through controlled incident, request, access, change, and release processes using established service-management workflows. Create and maintain runbooks, operating procedures, architecture context, ownership information, recovery procedures, and knowledge-transfer materials.



Mentor Middle-level engineers, review technical work, improve team practices, and ensure effective handoffs across the distributed service team. Participate in the Data Platform on-call rotation for critical incidents outside staffed service hours. MUST HAVES 5+ years of professional experience in Data Engineering or Data Platform Engineering . Strong hands-on experience operating and developing solutions on Snowflake , including data-layer design, warehouse performance, access patterns, and production troubleshooting. Advanced SQL skills and strong experience with dbt for transformation, testing, documentation, lineage, and controlled deployment. Experience operating managed ingestion tools such as Fivetran or HVR and supporting custom data-ingestion pipelines. Hands-on experience with AWS data services , particularly S3 and event-driven or file-based ingestion patterns. Experience orchestrating and troubleshooting data workloads with Argo Workflows on Kubernetes , or comparable workflow-orchestration technologies. Proficiency in Python or a comparable language for data engineering, automation, and operational tooling. Strong understanding of data modeling, pipeline dependencies, schema evolution, backfills, data validation, and production data quality. Experience with observability, logging, alerting, and incident-management practices for production data platforms. Demonstrated ability to lead complex incident resolution, perform root-cause analysis, and convert findings into preventive improvements. Ability to make well-reasoned technical decisions, identify tradeoffs, estimate work, and guide improvements across a complex platform. Experience mentoring engineers and collaborating effectively with Cloud

📌 Senior data platform engineer id92207 (México)
🏢 Agileengine
📍 México