Senior Data Engineer, Embedded

Shopify; United States; Teletrabalho; USD 86 000 - 128 000; Tempo integral

Resumo da oferta

Shopify in Canada is seeking a Data Engineer to contribute to Shopify’s Data Warehouse. You will design data models, build pipelines, and enable dashboards and reports that empower product insights across the organization.

You will collaborate with Product Data Scientists, Engineers, and Business Analysts to deliver production-quality data products and scalable dashboards, while evangelizing data engineering practices in a fast-paced environment.

Qualificações

• Commercial experience building scalable data warehouses.
• Dimensional modeling (Star Schema, Kimball, Inmon).
• Advanced SQL skills (windows, UDFs).
• Exposure to data engineering tooling: ingestion, testing, lineage, orchestration, publishing data, metric layers.
• Hands-on experience with real-time and batch data pipelines with tight SLAs.
• Strong collaboration and communication across multiple teams.
• Technical thought leadership, comfortable with ambiguity and mentoring.

Responsabilidades

• Understand business/objectives with partners and identify required data.
• Design, build, and document data models.
• Write data transformations using dbt or Spark.
• Ship data pipelines (real-time and batch).
• Optimize pipelines for freshness and cost.
• Collaborate with engineers to influence data production.
• Work with data engineers on tooling for data quality and automation.
• Adhere to Shopify data architecture standards.
• Partner with product, engineering, and business leaders to guide product decisions with data.
• Build production-quality dashboards and scalable data products.

Conhecimentos

SQL

Data modeling

Data warehousing

Team collaboration

Communication

Mentoring

Ferramentas

dbt

Spark

ETL tooling

Airflow

Descrição da oferta de emprego

Location: Canada

Opportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. We propel entrepreneurs and enterprises to scale the heights of their potential. Since 2006, we’ve grown to 10,000 employees and generated over $496 billion in sales for millions of merchants in 175 countries.

This is life-defining work that directly impacts people’s lives as much as it transforms your own. This is putting the power of the few in the hands of the many, is a future with more voices rather than fewer, and is creating more choices instead of an elite option.

About You

Moving at our pace brings a lot of change, complexity, and ambiguity—and a little bit of chaos. Shopifolk thrive on that and are comfortable being uncomfortable. That means Shopify is not the right place for everyone.

Before you apply, consider if you can:

• Care deeply about what you do and about making commerce better for everyone
• Excel by seeking professional and personal hypergrowth
• Keep up with an unrelenting pace (the week, not the quarter)
• Be resilient and resourceful in face of ambiguity and thrive on (rather than endure) change
• Bring critical thought and opinion
• Embrace differences and disagreement to get shit done and move forward
• Work digital-first for your daily work

About the role

Data is a crucial part of Shopify’s mission to make commerce better for everyone. We organize and interpret petabytes of data to provide solutions for our merchants and stakeholders across the organization. As a Data Engineer at Shopify, your primary responsibility will be to contribute to Shopify’s Data Warehouse. Your work will unlock powerful insights to guide the development and improvement of Shopify’s products.As an embedded Data Engineer, you will work directly building foundations for products that transform merchants' lives. You'll collaborate with a multidisciplinary team of professionals that can include Product Data Scientists, Machine Learning Engineers, Business Analysts, and Product Management. The data you shape will be used to power product analysis, dashboards, and reports. You will also contribute by performing product analysis and creating dashboards and reports yourself from time to time. Get excited about flexing these analysis and reporting muscles, while also evangelizing the values and excellence in the Data Engineering craft to your multidisciplinary peers in a fast moving environment with competing priorities.

Example day to day responsibilities include:

• Working with business partners to understand business and product objectives and identify the data needed to support them
• Designing, building, implementing, and documenting data models
• Writing data transformations using dbt or Spark
• Shipping data pipelines including real-time streaming and batch processing
• Optimizing data transformation pipelines to increase freshness or reduce computational time/cost
• Working with engineers to understand and influence how data is produced
• Collaborating with other data engineers on tooling for automated tasks around consuming, validating raw/modeled data, updating modeled data
• Subscribing to and implementing architecture and standards following the Data Engineer craft at Shopify
• Collaborating with sister disciplines (Engineering, Data Science) to establish best practices and evangelize the values and priorities of the Data Engineering craft
• Partnering closely with product, engineering and other business leaders to influence product and program decisions with data
• Building production-quality dashboards and scalable data products

Qualifications

• Commercial experience in Data Engineering, and/or Analytics Engineering, building scalable data warehouses
• Dimensional Modeling (Star Schema, Kimball, Inmon)
• Advanced SQL skills (ease with window functions, defining UDFs)
• Exposure to Data Engineering tooling: ingesting, testing transformations, lineage, orchestration, publishing data, metric layers
• Hands-on experience implementing real-time and batch data pipelines with tight SLOs and complex transformation requirements
• Fantastic collaboration and communication skills, demonstrated by successful large-scale projects spanning multiple teams
• Technical thought leader, comfortable navigating ambiguity and mentoring various level of team members
• Aptitude for product analysis, dashboarding, and reporting