Data Engineer

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

We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. Scribd Flex offers flexible work options in partnership with managers, with occasional in-person attendance required for all employees to build collaboration, culture, and connection.

We hire for “GRIT” – the blend of passion and perseverance toward long-term goals. The acronym outlines the standards we hold ourselves to: set and achieve Goals, achieve Results, contribute Innovative ideas and solutions, and positively influence the Team through collaboration and attitude.

As a Staff Data Engineer
, you’ll be both a hands-on technical expert and a strategic leader. You’ll drive the design of core data models and pipelines in our Databricks/Delta Lake lakehouse, setting the standards for quality, reliability, and scalability across the platform. You’ll own end-to-end solutions from architecture to operations and optimization, mentor engineers, and help guide the long-term direction of Scribd’s data ecosystem. You’ll contribute to evolving 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 that 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.

Required Skills

• 8+ years of experience in data engineering, with a strong background in 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.
• Proven experience designing data models and schemas that serve multiple downstream use cases (analytics, ML, APIs).
• Experience implementing modern data orchestration patterns for big data use-cases, including batch and streaming workloads.
• Demonstrated 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 with the ability to translate technical trade-offs to both engineers and non-technical stakeholders.

Desired Skills

• 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 the data engineering community.
• Experience operationalizing data observability through Datadog or equivalent monitoring tools.
• Experience working with Analytics teams to understand their requirements and translate to data products and data solutions.

Compensation, Benefits, and Perks

At Scribd, your base pay is one part of your total compensation package and is determined within a range. The pay ranges are based on local cost of labor benchmarks for each role, level, and location. California ranges: $167,000 to $260,500; US outside California: $137,500 to $247,500;
Canada: $175,000…