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Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.

Culture at Scribd, Inc.
We support a culture where our employees can be real and 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. We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance while committing to intentional in‑person moments that strengthen collaboration and culture. Occasional in‑person attendance is required for all Scribd, Inc. employees, regardless of location.

About the Team
Scribd’s Data Platform team builds and governs the analytical data models and transformation layers that power trusted metrics, experimentation, ML and product insights across Scribd, Everand and Slideshare. We’re midway through a multi‑year investment to modernize our data architecture for fully governed, properly‑modeled data that every team can trust and build upon. At Scribd, we leverage deep data insights to inform product development, experimentation and subscriber engagement.

What You’ll Do
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 standards for quality, reliability and scalability. You’ll own end‑to‑end solutions from architecture to operations, guide long‑term direction of Scribd’s data ecosystem, collaborate across teams to turn complex business problems into robust data solutions, mentor engineers, and help evolve toward a fully governed lakehouse.

You Will

Design and own canonical analytical data models, defining grain, keys and relationships that power Scribd’s enterprise metrics and reporting.

Implement modern data lake orchestration patterns, including medallion architectures.





Design and evolve scalable analytical data structures and transformation layers in Databricks/Delta Lake, ensuring correctness, performance and clarity of modeled datasets.

Define and enforce data modeling standards that ensure analytical correctness and prevent metric ambiguity.

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 contemporary lakehouse technologies.

Advanced SQL expertise including complex joins, aggregations, window functions, CTEs, query optimization and reasoning about data at different aggregation levels.

Deep experience designing dimensional and analytical data models and owning metrics across domains (analytics, ML, APIs).

Experience designing reliable transformation workflows that maintain consistent data and business logic across batch and streaming pipelines.

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.

This role requires hands‑on data modeling and SQL fluency; it is not a platform‑only or infrastructure‑focused position.

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 requirements and translate to data products and solutions.

Salary Range
In California: $167,000 to $260,500. Outside California in the U.S.: $137,500 to $247,500. In Canada: $175,000 CAD to $231,500 CAD.

Benefits

Scribd Flex (flexible work model)

Comprehensive health, dental and vision coverage

Mental health support and disability coverage

Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time and sabbaticals

Paid parental leave and family support benefits

Retirement matching and employee equity

Learning and development programs and professional growth opportunities

Wellness and home office stipends

Complimentary access to the Scribd, Inc. suite of products

Enterprise access to leading AI tools

Location Availability
Employees must reside in or near one of the following cities: Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington D.C., Ottawa, Toronto, Vancouver, or Mexico City.

Equal Employment Opportunity
We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make by emailing [email protected].

We are committed to equal employment prospect regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas.

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Apply on Kit Job: kitjob.ca/job/2jrp2x

📌 Staff Data Engineer (British Columbia)
🏢 Scribd
📍 British Columbia