Data Engineering Specialist | Spécialiste en Ingénierie de Données

WorkJam, Inc.•Montreal, QC

10d•Hybrid

About The Position

WorkJam is the world’s leading digital frontline workplace, connecting the entire deskless workforce to work in perfect harmony. WorkJam solves the most common problems faced by global frontline enterprises through scheduling tools, task management, communication, and learning – all within a single app! We’re proud of our dedicated teams who are making a difference for millions of frontline employees, which led us to be recognized as the 2024 World Future Award winner for Innovation in Workforce Management, among other accolades. As a key member of our data engineering team, the ideal candidate will possess a profound knowledge in data processing frameworks and analytics, with extensive experience in Python, Airflow, Apache Beam, DBT, GCP Dataflow, and GCP BigQuery. You will play a crucial role in designing and implementing sophisticated data pipelines, overseeing data architecture, and guiding the team towards achieving optimal data processing and analytics solutions.

Requirements

Bachelor's or master’s degree in computer science, Engineering, or related field.
5+ years of experience in a Data Engineering role with a focus on leadership and project management.
Excellent project management and organizational skills.
Robust analytical skills for working with unstructured datasets and complex data systems.
Exceptional communication skills to clearly convey technical concepts to a broad range of stakeholders.
Strong leadership presence with a sense of ownership and accountability, able to independently drive projects while contributing to team success.
Experience in leading and supporting cross-functional teams in a dynamic environment.
Advanced SQL knowledge and experience with relational databases and familiarity with various databases.
Demonstrated expertise in building and optimizing large-scale data pipelines, architectures, and datasets.
Knowledge of message queuing, stream processing, and highly scalable big data storage solutions.
Proficiency in data pipeline and workflow management tools such as Airflow and Luigi.
Deep experience with GCP cloud services including Dataflow, BigQuery, and PubSub.
Strong English skills to interact with global colleagues outside Quebec. Proficiency in both spoken and written English is essential.

Nice To Haves

Experience with big data tools like Hadoop, Spark, Kafka, etc., is highly regarded.
Familiarity with stream-processing systems like Storm and Spark-Streaming is advantageous.

Responsibilities

Lead the design, construction, testing, and maintenance of highly scalable and robust data pipeline architectures for both batch and real-time data processing.
Lead the strategic planning of data architectures, pipeline designs, and data modeling to support business initiatives and ensure compliance with data governance and security policies.
Mentor junior data engineers, fostering a culture of technical excellence, innovation, and continuous improvement.
Collaborate with data architects, business stakeholders, and IT teams to understand data needs, implement data strategies, and ensure seamless data integration across the organization.
Spearhead the evaluation, implementation, and management of new technologies and tools to enhance the capabilities of the data infrastructure.
Architect and maintain sophisticated data pipelines, optimizing data extraction, transformation with DBT, and loading into SQL, Python, and GCP BigQuery environments.
Optimize data flows and architecture to improve data quality, efficiency, and reliability across complex and diverse data systems.
Lead the development of complex data processing tasks using Apache Beam and GCP Dataflow for real-time analytics and insights.
Utilize DBT to manage data transformation workflows, ensuring accuracy and consistency in GCP BigQuery.
Develop and maintain scalable and automated data pipelines, incorporating advanced data quality checks and monitoring to minimize data discrepancies and errors.
Drive innovation by exploring new data technologies and software engineering practices to enhance the agility and capability of the data engineering team.
Act as a thought leader in data engineering, staying ahead of industry trends and integrating new technologies and approaches to evolve our data infrastructure.