Data Engineer

Overview

Staff Data Engineer role at Scribd, Inc.

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At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our products:
Everand, Scribd, Slideshare, and Fable.

We support a culture where employees can be real and bold; we debate and commit as we embrace plot twists, and every employee is empowered to take action with a customer-first mindset. Scribd Flex provides flexibility in work style with some in-person attendance required to build collaboration, culture, and connection.

We hire for “GRIT” — goals, results, innovation, and teamwork. We value individuals who pursue a GRIT-ty approach to work.

As a Staff Data Engineer, you’ll be both a hands-on technical expert and a strategic leader. You’ll design core data models and pipelines in our Databricks/Delta Lake lakehouse, setting standards for quality, reliability, and scalability. You’ll own end-to-end solutions from architecture to operations and optimization, collaborate across teams to translate complex business problems into robust data solutions, and mentor engineers to raise performance.

You’ll help evolve Scribd’s data ecosystem toward a fully governed lakehouse with fine-grained access controls and consistent lineage.

You will:

• Design and implement core data models and pipelines that power analytics, ML, and product experiences.
• Implement modern data lake orchestration patterns, including medallion architectures.
• Architect and evolve a scalable, cost-efficient, and reliable lakehouse foundation using Databricks, Delta Lake, and Airflow.
• Define best practices and technical standards to improve data quality, governance, and performance across teams.
• Mentor engineers and foster a culture of ownership, operational excellence, and continuous learning.
• Shape the long-term technical vision and roadmap for Scribd’s data platform.

• 8+ years of experience in data engineering with strong data architecture, data modeling, and distributed data systems.
• Deep expertise in Databricks, Delta Lake, Spark, and modern lakehouse technologies.
• Advanced proficiency in SQL and Python or Scala, including performance optimization and large-scale ETL design.
• Experience designing data models and schemas for multiple downstream use cases (analytics, ML, APIs).
• Experience implementing modern data orchestration patterns for batch and streaming workloads.
• Ability to lead technical initiatives, set standards, and influence decisions across teams.
• Comfort owning systems end-to-end, including monitoring, reliability, and cost management.
• Excellent communication skills to translate technical trade-offs to engineers and non-technical stakeholders.

• Experience with subscription, payments, or large-scale consumer data domains.
• Familiarity with AWS data services (S3, Glue, EMR, Kinesis) and cloud cost optimization.
• Knowledge of streaming architectures (Kafka, Kinesis, or similar).
• Experience implementing data quality, governance, and observability standards at scale.
• Contributions to open-source projects or thought leadership in data engineering.
• Experience operationalizing data observability through Datadog or equivalent tooling.
• Experience collaborating with Analytics teams to translate requirements into data products.

We consider a wide range of factors when determining compensation, including experience, skills, and local market conditions. Salary ranges are provided for guidance and may vary by level and location. This position is eligible for a competitive equity package and a comprehensive benefits package. Geographic ranges below are for reference and may be adjusted for different levels or locations.

San Francisco, CA area: approximately $167,000 – $260,500. United States (outside CA): approximately $137,500 – $247,500. Canada: approximately CAD 175,000 – CAD 231,500. Final compensation will reflect role, location, and level.

Are you currently based in a location where Scribd…