Senior Data Platform Engineer ID92207

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you

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.
• Demonstra