Staff Data Engineer

Workiva Inc.

1d•$129,000 - $207,000•Remote

About The Position

Workiva is seeking a Staff Data Engineer to lead the implementation of high-value, data-driven features within our product domain. While our Platform team builds the underlying infrastructure, you are the expert consumer who transforms that infrastructure into tangible solutions for our customers. In this role, you will operate with significant autonomy to design and execute data solutions that power customer-facing applications, advanced analytics, and AI/ML features. You will act as a strategic partner to Product Owners and Application Engineers, ensuring that our data strategy is seamlessly integrated into the Workiva product experience.

Requirements

8+ years of experience in Data Engineering, with a strong emphasis on building solutions for customer-facing products or applications.
Independent Execution: Proven ability to lead large-scale projects from concept to completion with minimal supervision, navigating ambiguity across multiple teams.
Strategic Communication: Exceptional ability to evangelize data strategy to non-technical stakeholders and influence application engineers on data best practices.

Nice To Haves

Mastery of the Stack: Deep expertise in Snowflake, dbt, and Kafka for building real-world, high-value data products.
Software Mindset: Strong Python and SQL skills, with a focus on building reusable libraries, automation, and CI/CD-driven workflows.
Product Sense: Experience working in a SaaS product environment, understanding how data latency and accuracy impact the end-user experience.
Data Modeling: Advanced understanding of modeling for both analytical (OLAP) and application-support (high-concurrency) use cases.

Responsibilities

Domain Solution Leadership Drive Product Value: Architect and build high-performance data solutions that directly power customer-facing features, utilizing the internal data platform (Snowflake, dbt, Kafka).
End-to-End Execution: Lead the design and delivery of complex data projects independently, from initial discovery with stakeholders to production deployment and monitoring.
Stakeholder Alignment: Partner with Product Managers and Business Leaders to translate customer needs into technical data requirements, ensuring "Data-as-a-Product" excellence.
Technical Integration & Evangelism Bridge the Gap: Work embedded with Application Engineering teams to advocate for upstream data quality and ensure application architectures support downstream data needs.
Influence the Platform: Act as the "Lead Customer" for our internal Data Platform team—identifying gaps in the platform’s capabilities and contributing to its strategic roadmap based on product requirements.
Best Practices: Establish and evangelize standards for data modeling, observability, and performance within the product domain.
Scalable Delivery: Design resilient, production-grade pipelines using DLT and Snowpipe that handle enterprise-scale workloads with low latency.
Complex Transformations: Own the domain’s dbt layer, ensuring code is modular, tested, and optimized for high-performance serving in Snowflake.
Mentorship: Guide and elevate Senior and Mid-level engineers on the team through code reviews, design docs, and technical coaching.