Principal Data Engineer / Architect

Scribd, Inc.; Ottawa; Hybrid; CAD 90,000 - 150,000; Full time

Job summary

An established industry player is seeking a Principal Data Engineer/Architect to lead the design and development of robust data architectures. This pivotal role involves shaping data strategies and guiding stakeholders on data consumption while collaborating with cross-functional teams. With a focus on building scalable data systems and enhancing customer satisfaction, you'll have the opportunity to make a significant impact on the organization's data initiatives. If you have a passion for data engineering and a proven track record in architecting complex data solutions, this is the perfect opportunity to join a forward-thinking team committed to innovation and excellence.

Qualifications

• 10+ years in data engineering with a focus on data architecture and management.
• Expertise in Scala or Python with hands-on Spark experience.
• Experience with data lake technologies and real-time processing.

Responsibilities

• Lead design and development of data architecture for modern data products.
• Shape data strategy and guide stakeholders on data consumption.
• Collaborate with cross-functional teams to design data models and storage solutions.

Skills

Data Engineering

Data Architecture

Data Modeling

Scala

Python

Spark

SQL

Data Lake Technologies

Streaming Platforms

AWS Data Services

Tools

Databricks

Delta Lake

Jira

Slack

Git

Docker

Jenkins

Terraform

Qlik

Tableau

Job description

Join to apply for the Principal Data Engineer / Architect role at Scribd, Inc.

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 three products: Everand, Scribd, and Slideshare.

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.

When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location.

So what are we looking for in new team members? Well, we hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd, we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT-ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here’s what that means for you: we’re looking for someone who showcases the ability to set and achieve Goals, achieve Results within their job responsibilities, contribute Innovative ideas and solutions, and positively influence the broader Team through collaboration and attitude.

What You'll Do

As a pivotal member of the team, you will lead the design and development of a robust data architecture that guides data modeling, integration, processing, and delivery standards enabling modern data product development at Scribd.

You will also serve as a data and analytics solution architect, leading architecture initiatives encompassing data warehousing, data pipeline development, data integrations, and data modeling. You will shape Scribd’s data strategy, guiding stakeholders in how they consume and act on data.

We’re looking for someone with proven proficiency in architecting, designing and development experience with batch and real time streaming infrastructure and workloads. Your expertise will help establish standards for data modeling, integration, processing, and delivery and also help translate business requirements into technical specifications.

At Scribd, we leverage deep data insights to inform every aspect of our business, from product development, experimentation, to understanding our subscriber engagement and tracking key performance indicators. You'll join a data engineering team tackling complex challenges within a rich domain encompassing three distinct brands – Scribd, Everand, and Slideshare – all serving a massive user base with over 200 million monthly visitors and 2 million paying subscribers. You'll have the opportunity to make a real impact as we are heavily investing in improving our core data layer and this exciting new role puts you right at the forefront of this initiative.

Based on the project, this might involve cross-functional work with the Data Science, Analytics, and other Engineering and Business teams to design cohesive data models, database schemas and data storage solutions, consumption strategies and patterns. Almost everything you will be working on will be to increase the "customer satisfaction" for internal customers of Scribd data.

Required Skills

• 10+ years of experience in data engineering, with a strong background in data architecture, data modeling, and data management, building and scaling robust data systems for complex business domains.
• Expertise in Scala or Python, with a deep understanding and hands-on experience in Spark for designing, optimizing, and scaling large-scale data processing pipelines, and proficiency in at least one SQL dialect.
• Experience with data lake technologies (e.g., Databricks, Delta Lake), data storage formats (Parquet, Avro), query engines (such as Photon, Spark SQL), and both real-time streaming and batch processing, or equivalent technologies and frameworks.

Desired Skills

• Experience and working knowledge of streaming platforms, typically based around Kafka.
• Strong grasp of AWS data platform services and their strengths/weaknesses.
• Hands on experience in implementing data pipelines for data ingestion and transformation to support analytics and ML pipelines.
• Strong experience communicating asynchronously using collaboration tools like Jira, Slack, etc.
• Experience using automation and CI/CD tooling like Git, GitHub, Docker, Jenkins, Terraform, etc.
• Experience developing standards for database design and implementation of various strategic data architecture initiatives around data quality, data management policies/standards, data governance, privacy and metadata management.
• Working experience integrating with BI frameworks like Qlik, ThoughtSpot, Looker, Tableau, etc.