Senior Manager, Data Engineering & Data Platform

The Role
SolarWinds is mid‐stream in a major shift from a legacy SQL Server data warehouse + SSIS to a modern data platform on Google BigQuery and dbt, with Tableau, Vertex AI, and Glean on top.

We’re hiring a Senior Manager, Data Engineering & Data Platform to own the data transformation lifecycle :

• BigQuery projects and datasets across raw, staging, intermediate, and mart layers.
  Ingestion from core business systems via CData, BigQuery DTS, and modern ELT.
• dbt projects and CI/CD that turn raw data into governed, trusted marts for GTM, Finance, TR, and Exec reporting.
• This is a highly technical leadership role focused on data production, building and running the platform and core models that BI, Analytics Engineering, and Data Science depend on.

Core Responsibilities

1. Own the BigQuery & dbt Platform

• Act as operational owner for BigQuery:
• Project and dataset layout for raw / staging / intermediate / mart zones.
• IAM, service accounts, and Dataplex taxonomy.
• Performance tuning (partitioning, clustering, query design) and cost optimization.
• Define and enforce a medallion architecture and dbt project structure that scales across domains (GTM, Finance, TR, People).

2. Lead Ingestion & Transformation

• Lead implementation of our Enterprise Ingestion Strategy:
• CData and BQ DTS for core systems (e.g., Salesforce, NetSuite, key SQL sources).
• Airbyte and vendor exports for SaaS/event sources (e.g., Pendo, Gong, Zoom).
• Own the core dbt projects:
• Structure models from staging → intermediate → marts with strong tests and documentation.
• Maintain CI/CD for dbt (build, test, deploy) and set code review standards.

3. Reliability, SLAs & Data Quality

• Define and track SLOs/SLAs for freshness, completeness, and reliability of critical GTM/Finance and TR-facing marts.
  Embed:
  dbt tests (schema, PK/FK, business rules, freshness).
• Monitoring and alerting for pipeline health and anomalies.
• Runbooks and incident workflows for the data platform.
• Drive continual reduction in incident volume and MTTR for data issues.

4. Cost & Performance Management

• Own BigQuery cost visibility and optimization:
• Track compute and storage spend by project/domain.
• Identify and fix expensive queries and anti‐patterns.
• Work with Finance/IT on budgets and cost guardrails.

5. Governance & Security

• Implement and uphold data governance standards with the Data Governance team:
• Dataset/model ownership, stewardship, and certification.
• BigQuery security patterns (policy tags, row/column security, roles).
• Catalog completeness for migrated assets in Dataplex/metadata tooling.
• Ensure sensitive domains (e.g., HR/People, Gong/Zoom, security‐relevant data) follow elevated controls and approved sharing patterns.

6. Lead the Data Engineering & Platform Team

• Manage and mentor a 6–9 person Data Engineering team (Austin, Cork, Philippines), with potential to grow.
• Set priorities, align work to business outcomes, and build a high‐standards engineering culture (tests, docs, reviews, KT).
• Operate a pod‐based, sprint‐driven model:
• Integrate internal DEs, an embedded Lirik pod, and TR contributors into one coherent execution plan.
• Balance migration, new domain onboarding, and platform hardening.

7. Deliver the Migration & Platform Roadmap

• Co‐own execution of the SQL DW → BigQuery/dbt migration:
• Domain‐by‐domain migration and Tableau rewiring for GTM/Finance and TR.
• Parallel‐run and validation where needed.
• Planned decommissioning of SQL DW/SSIS workloads.
• Make BigQuery + dbt the authoritative warehouse of record for core GTM/Finance and TR data.
• Prepare domains and marts to be AI/NLQ‐ready for Glean/Vertex/Gemini.

Required Experience
Must‐Have Experience

• 7–10+ years in data engineering or closely related roles, including 3+ years leading engineers or owning a data platform.
• Hands‐on experience designing, building, and operating a cloud data warehouse (BigQuery strongly preferred) with dbt or an equivalent framework.
• Proven track record migrating from a legacy SQL data warehouse to a cloud DW, including cutover and decommissioning.
• Strong technical depth in:
• SQL and data modeling (dimensional modeling, medallion patterns).
• BigQuery performance and cost optimization (partitioning, clustering, query tuning).
• Modern ELT tools/patterns (e.g., CData/DTS/Airbyte or equivalents).
• CI/CD for data (dbt tests, deployments, environment management).
• Demonstrated ability to manage and develop a team of 5–10 engineers across locations.
• Excellent communication and stakeholder skills with GTM, Finance, BizApps, Governance, and/or Product teams.

Nice to Have

• Experience in B2B SaaS or subscription businesses, especially GTM/Finance data (ARR/NRR, pipeline, renewals, Q2R).
• Familiarity with Tableau as a consumer of warehouse marts.
• Exposure to AI/ML on warehouse data (BigQuery ML, Vertex AI) and/or AI‐powered analytics tools (e.g., Glean).
• Experience working with consulting pods or off‐shore teams in a pod-based or matrix model.
• If you like building modern data platforms on BigQuery + dbt, enjoy balancing platform rigor with real business outcomes, and want to lead a team through a high‐impact migration and modernization, we’d like to talk.