Lead Data Platform Engineer

Your role at Dynatrace

The Lead Platform Engineer, Data is a hands-on engineering and operations role responsible for the reliability, governance, automation, and continuous improvement of a large-scale, Tier 1 enterprise data platform. The role requires deep experience managing Snowflake, dbt, ETL/data integration platforms, and production data services, with strong expertise in platform administration, operational excellence, incident management, and automation. The ideal candidate combines practical platform engineering skills with a systems-thinking mindset to improve reliability, scale, and efficiency while reducing manual effort through automation and modern AI-driven capabilities.

Key Responsibilities

• Snowflake Platform Administration: Own Snowflake administration end-to-end across development, staging, and production environments, leveraging strong Snowflake and enterprise database administration experience to ensure platform reliability, security, performance, and cost efficiency. Manage multiple Snowflake accounts, including account consolidation, migration, and modernization initiatives, while driving warehouse optimization, query tuning, RBAC and Row-Level Security implementation, access automation, and platform monitoring. Serve as the primary escalation point for platform issues and lead adoption of advanced Snowflake capabilities such as Streams, Tasks, Dynamic Tables, and Snowpipe. Hands-on Snowflake administration experience, prior database administration expertise, and SnowPro Administrator certification are required.
• dbt Administration & Governance: Administer the dbt project and deployment infrastructure - owning project configuration, model architecture standards, environment and job management, test coverage enforcement, documentation requirements, and model promotion workflows across environments. Monitor dbt run health and lineage, configure observability tooling (Elementary or equivalent), and partner with analytics engineers to review and certify models for production use.
• Data Stack Automation & Operations: Automate the operational layer of the data technology stack - including ETL/ELT tool administration (Fivetran, etc), RBAC and access provisioning via Terraform or scripting, alerting and notification pipelines for data quality and platform health, and CI/CD release workflows for dbt and platform configuration. Reduce manual operational toil by building reusable automation frameworks that make routine platform tasks fast, auditable, and self-service where appropriate.

This is a remote eligible position.  Candidates who sit within a 45 mile radius of Boston, MA; Denver, CO; Detroit, MI will be required to work hybrid (2 days per week in office).  All candidates will be required to work EST hours.  

What will help you succeed

Minimum Requirements

• 6+ years of hands-on data platform experience, with direct ownership of a Snowflake environment at production scale.

Preferred Requirements 

• Deep Snowflake DBA skills: virtual warehouse sizing, auto-suspend and scaling policy configuration, multi-cluster warehouse management, and credit cost governance.
• Snowflake RBAC design: functional role hierarchy design, privilege grants, service account management, and systematic access provisioning - not one-off manual grants.
• Row-Level Security implementation using Snowflake row access policies for multi-tenant or restricted datasets.
• Snowflake feature ownership: Streams, Tasks, Dynamic Tables, Snowpipe, External Stages, Data Sharing, and Secure Views in production workloads.
• Query profiling and optimization: reading query profiles, identifying bottleneck operators, applying clustering keys, and resolving warehouse contention.
• Deep experience administering dbt project end-to-end - model architecture (staging / intermediate / marts), incremental and snapshot patterns, and environment separation.
• dbt Cloud or dbt Core deployment administration: job scheduling, environment variable management, run monitoring, failure alerting, and model promotion across dev/staging/prod.
• Test coverage governance: enforcing schema tests, data tests, and source freshness checks as mandatory gates before production promotion.
• dbt observability: configuring and maintaining Elementary, or equivalent tooling - including artifact-based monitoring, model health dashboards, and freshness tracking against Snowflake account usage.
• Experience partnering with analytics engineers to review model design, optimize underperforming models, and maintain lineage and metadata for downstream governance.
• Platform-level administration of Fivetran or Matillion: connector governance, sync scheduling, schema drift policy, user and environment management,