Overview: Join Sparq as a Principal Data Engineer to own data pipelines end to end—from ingestion and transformation through validation and delivery—bridging modern cloud data architecture with legacy enterprise reporting systems in active use.

Responsibilities

Requirements and attributes:

Architect scalable data solutions, including data models, partitioning and clustering strategies, and query optimization for datasets ranging from terabytes to petabytes.
Leverage Google Cloud Platform and BigQuery to inform architectural decisions, including partitioning, clustering, and cost management.
Build and operate production ETL/ELT pipelines from ingestion to final delivery, taking full ownership of the end-to-end system.
Use advanced SQL to develop complex queries; work with SQL Server, stored procedures, and SSRS in legacy environments.
Develop data monitoring, reconciliation, and automated quality assurance frameworks to ensure data integrity.
Manage ETL/ELT processes at production scale; apply Python for automation and data processing.
Work with data visualization tools such as Power BI, Looker, or Tableau, and translate insights for stakeholders.
Collaborate with cross-functional teams; mentor and lead teammates; advocate for AI-enabled workflows using tools like Claude through the Anthropic partnership.
Embrace AI to streamline tasks, improve output quality, and share best practices.
Consultative problem-solving skills with the ability to align digital solutions to client goals.
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About the role

Principal Data Engineer (GCP & BigQuery) – Remote OK (United States)

Overview: Join Sparq as a Principal Data Engineer to own data pipelines end to end—from ingestion and transformation through validation and delivery—bridging modern cloud data architecture with legacy enterprise reporting systems in active use.

Responsibilities

Requirements and attributes:

Architect scalable data solutions, including data models, partitioning and clustering strategies, and query optimization for datasets ranging from terabytes to petabytes.
Leverage Google Cloud Platform and BigQuery to inform architectural decisions, including partitioning, clustering, and cost management.
Build and operate production ETL/ELT pipelines from ingestion to final delivery, taking full ownership of the end-to-end system.
Use advanced SQL to develop complex queries; work with SQL Server, stored procedures, and SSRS in legacy environments.
Develop data monitoring, reconciliation, and automated quality assurance frameworks to ensure data integrity.
Manage ETL/ELT processes at production scale; apply Python for automation and data processing.
Work with data visualization tools such as Power BI, Looker, or Tableau, and translate insights for stakeholders.
Collaborate with cross-functional teams; mentor and lead teammates; advocate for AI-enabled workflows using tools like Claude through the Anthropic partnership.
Embrace AI to streamline tasks, improve output quality, and share best practices.
Consultative problem-solving skills with the ability to align digital solutions to client goals.
-

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