Data Platform Manager: Trusted Pipelines & Analytics

Scribd, Inc.; Chicago (IL); Hybrid; USD 154,500 - 252,000; Full time

Benefits offered by this job

Flexible work model

Comprehensive health, dental, and vision coverage

Generous paid time off

Job summary

Scribd, Inc. in Chicago is looking for a Senior Manager, Data Engineering to lead a team focused on building reusable data products for analytics and AI. This role requires 10+ years of experience in data roles and strong technical leadership skills.

The ideal candidate will guide architecture, establish engineering standards, and foster a high-performing team. They must have expertise in data platforms and production-grade data pipelines, and experience with tools like Databricks and Spark.

Qualifications

• 10+ years of experience in Data Engineering, Data Platform, or related data roles.
• 3+ years leading engineering teams, including coaching, performance management, and organizational development.
• Deep expertise building scalable data platforms and production-grade data pipelines.
• Strong experience with dimensional modeling, data architecture, and designing reusable analytical datasets.

Responsibilities

• Provide technical, delivery, and people leadership for the Data Engineering team.
• Establish engineering standards for data modeling and pipeline design.
• Drive architecture discussions and design reviews.
• Lead execution across multiple concurrent initiatives.

Skills

Data Engineering

SQL

Python

Dimensional modeling

Data architecture

Education

10+ years experience in Data Engineering

3+ years leading engineering teams

Tools

Databricks

Spark

Snowflake

Job description

Scribd, Inc. in Chicago is looking for a Senior Manager, Data Engineering to lead a team focused on building reusable data products for analytics and AI. This role requires 10+ years of experience in data roles and strong technical leadership skills.

The ideal candidate will guide architecture, establish engineering standards, and foster a high-performing team. They must have expertise in data platforms and production-grade data pipelines, and experience with tools like Databricks and Spark.