Senior Manager, Analytics Engineering

At Solar Winds, we're a people-first company. Our purpose is to enrich the lives of the people we serve-including our employees, customers, shareholders, partners, and communities. Join us in our mission to help customers accelerate business transformation with simple, powerful, and secure solutions.

The ideal candidate thrives in an innovative, fast-paced environment and is collaborative, accountable, ready, and empathetic. We're looking for individuals who believe they can accomplish more as a team and create lasting growth for themselves and others. We hire based on attitude, competency, and commitment. Solarians are ready to advance our world-class solutions in a fast-paced environment and accept the challenge to lead with purpose.

If you're looking to build your career with an exceptional team, you've come to the right place. Join Solar Winds and grow with us!

Role Overview

Solar Winds is undergoing a transformative shift in its data platform - migrating from SQL Server and SSIS to a modern ELT stack built on Google Big Query, dbt, and CData. This role leads the team responsible for executing that migration and operating the resulting platform at scale.

The Senior Manager, Analytics Engineering owns the intersection of data engineering and analytics: ingestion reliability via CData, transformation governance through dbt, and the delivery of analytics-ready datasets that serve Business Intelligence, Data Science, and Data Governance functions. This is not a maintenance role - it is a build role that will define how Solar Winds manages enterprise data for years to come.

Reporting directly to the Head of Enterprise Data, this leader manages a distributed team of five Data Engineers across Austin, TX and Manila, Philippines, and serves as a peer to leaders in BI, Data Science, and Data Governance.

Core Responsibilities

Platform Migration Leadership

* Own the end-to-end migration plan from SQL Server/SSIS to Big Query/dbt/CData, including sequencing, data validation, cutover strategy, and rollback planning.

* Define and enforce migration quality standards: row-count reconciliation, business rule parity, pipeline SLAs.

* Manage stakeholder communication on migration status, risks, and timelines.

dbt Governance & Transformation Layer Ownership

* Architect and govern the dbt project structure: sources, staging, intermediate, and mart layers.

* Establish and enforce dbt development standards: naming conventions, testing requirements, documentation coverage, and CI/CD pipeline integration.

* Own the semantic/metrics layer as a governance artifact - ensuring definitions align with Data Governance policies and BI consumption patterns.

Data Engineering Operations

* Oversee CData Sync configuration, connector management, and ingestion reliability across all data sources.

* Manage Big Query cost optimization: partitioning strategies, clustering, slot reservations, and query performance.

* Maintain pipeline observability - alerting, data quality monitoring, and incident response SLAs.

People & Team Leadership

* Directly manage 5 Data Engineers across Austin and Manila; build individual development plans and career pathways.

* Partner with the Head of Enterprise Data on headcount planning, hiring, and team structure as the platform matures.

Cross-Functional Partnership

* Serve as the primary interface between the Data Engineering team and leaders in BI, Data Science, Data Governance and GTM / Technology Leaders.

* Translate upstream data platform decisions into downstream data contract commitments - SLAs, schema stability, freshness guarantees.

* Participate in enterprise data governance forums and contribute to data lineage, data quality, and data classification initiatives.

Governance

* Maintain data lineage metadata for all dbt models as a first-class artifact, consumable by the Data Governance function.

* Ensure Big Query dataset-level access controls align with data classification policies (PII, confidential, internal, public).

* Support GDPR, SOX, and applicable data privacy requirements through pipeline design - including right-to-erasure patterns, audit logging, and data retention enforcement.

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