Staff Data Engineer, Workiva

• 5+ years in data engineering or analytics engineering, with hands-on ownership of dbt projects and data-model lifecycles
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field—or equivalent professional experience

Desirable

• As a Staff Data Engineer on the Workiva Carbon Engineering Team, you’ll partner closely with product engineers to design, build, and evolve the data products that power our mission-critical applications.
• You’ll leverage our central self-service data platform—built on dbt, DLT, Snowflake, Kafka, and more—to craft and maintain complex dbt models, enforce data quality guardrails, and deliver high-performance data products tailored to application use cases.
• You’ll operate within a highly regulated environment, collaborating through well-governed CI/CD pipelines without direct production access, and ensuring every release meets stringent compliance standards
• Model & Transform: Design, build, and evolve a complex suite of dbt models—implementing best practices for testing, version control, and lineage tracking—to serve application-specific data needs
• Ingestion & Processing: Author and maintain DLT pipelines for reliable batch and real-time ingestion. Developing automation and operational tasks in Python and Dagster
• SQL Mastery: Write, optimize, and document advanced SQL queries and scripts to support ad-hoc analyses, model performance tuning, and data validation routines
• APIs & Interfaces: Build APIs to expose curated data products to downstream applications and services
• Quality Frameworks: Implement data quality checks, monitoring, and alerting within dbt (e.g., custom tests, freshness checks) to enforce SLAs
• Governed Releases: Navigate complex, regulated release pipelines using GitOps/CI/CD workflows—author pull requests, manage promotions through dev/test/prod, and collaborate with platform/infrastructure teams for gated approvals
• Security & Controls: Adhere to information-protection policies, ensuring role-based access controls, audit logging, and encryption standards are in place
• Cross-Functional Partnership: Work hand-in-hand with product managers, software engineers, data analysts, and central data platform teams to translate application requirements into scalable data solutions
• Best Practices Evangelist: Mentor peers on dbt coding conventions, SQL performance tuning, and deployment processes; participate in code reviews and design discussions
• Platform Feedback: Relay application-team insights back to the central data platform roadmap—identifying enhancements to tooling, documentation, or self-service capabilities
• Continuous Learning: Stay current on emerging data-engineering technologies, advanced analytics patterns, and regulatory trends, and propose pilot projects to advance our analytics maturity