Staff Data Engineer

Webflow is the agentic web marketing platform for modern marketing teams, helping organizations build, manage, and optimize high-performing web experiences that drive predictable growth and strengthen brand trust. Building at Webflow will need grit, because we move fast, without ever sacrificing craft or quality. We’re looking for a Staff Data Engineer who can take ownership of and drive key initiatives across Data Platform Engineering, including the data lake, event instrumentation, data quality, and data governance. About the role: Location: Remote-first (United States) Full-time Permanent Exempt The cash compensation for this role is tailored to align with the cost of labor in different geographic markets. We've structured the base pay ranges for this role into zones for our geographic markets, and the specific base pay within the range will be determined by the candidate’s geographic location, job-related experience, knowledge, qualifications, and skills. United States (all figures cited below are in USD and pertain to workers in the United States) Zone A: $212,500 - $255,000 Zone B: $200,000 - $240,000 Zone C: $186,500 - $224,000 This role is also eligible to participate in Webflow's company-wide bonus program. Target amounts are a percentage of base salary and vary by career level. Payouts are based on company performance against established financial and operational goals. Please visit our Careers page for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. Application Information: Application deadline: applications accepted on an ongoing basis until position is closed and filled This posting is for an existing vacancy Reporting to the Manager, Data Engineering As a Staff Data Engineer, you'll… Design data solutions across batch, streaming, and real-time workloads. Design and build reliable data pipelines using Spark, Kafka, Iceberg, and Airflow/MWAA. Contribute to the evolution of data lake and data platform, including ingestion, processing, storage, and serving patterns. Build, maintain and scale cloud data infrastructure that includes Kafka, Airflow, Druid, EMR on EKS with reliability and observability in mind. Implement and improve data quality, observability, reliability, and governance across data pipelines and systems. Define and improve standards for event instrumentation and governance, including event schemas, validation, and schema evolution. Build platform solutions for centralized PII handling, data retention, deletion, and residency to support GDPR and CCPA requirements. Build agent harness capabilities that encode data engineering standards, pipeline patterns, and quality gates into AI tools, enabling agents to safely generate, review, and operate on data platform code. Contribute hands-on to complex technical initiatives, troubleshoot production issues, mentor engineers, and collaborate with Analytics Engineering and other teams. Build agent harnesses that embed data engineering standards, pipeline patterns, and quality gates into AI-assisted development workflows. About you: Requirements: BA/BS degree or equivalent experience 5+ years of experience in data engineering, building and operating production data pipelines and platforms. Strong experience with Spark and distributed data processing, including designing and optimizing production data pipelines. Experience designing batch, real-time, and streaming data solutions using technologies such as Kafka, Spark Structured Streaming, and CDC. Experience implementing data quality, observability, and reliability across production data systems. Experience with cloud data infrastructure, CI/CD, and infrastructure-as-code. Demonstrated experience leading complex data engineering initiatives end-to-end, from technical design and decomposition through implementation and production. Strong Programming and SQL skills, with experience building reliable and maintainable data systems. You’ll thrive as a Staff Data Engineer if you: Can navigate ambiguous problems, evaluate architectural tradeoffs, and make pragmatic technical decisions. Think beyond individual pipelines and consider the end-to-end data ecosystem, from data producers through downstream consumers. Bring experience with data governance and privacy, including PII management, retention, deletion, data residency, GDPR, or CCPA. Bring experience with event instrumentation and governance