Senior Data Platform Engineer ID92207

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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 LatAm 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 / DevOps 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