Data Engineer

Quill is a valuable part of the Staples family of brands. Since 1956, Quill has held its position as a leader in office supplies.

Acquired by Staples in 1998, Quill delivers the essential services, solutions, and award-winning customer support to help our customers – and our people – thrive. When you partner with Quill, as a customer or as an associate, every workday is more rewarding. 

From paper, ink & toner, to technology and custom print, we deliver the right products at the right value, plus an unmatched assortment of over 900 instant rewards available with every order.

Quill is hiring a talented and motivated Data Engineer. The selected candidate will be a smart, self-driven individual that will be responsible for data engineering tasks on multiple technology initiatives with medium-high complexity using accepted processes and methods.  The Data Engineer is responsible for developing and implementing data pipelines for AI, data science & data visualization projects. This position will be working on our transactional & analytical complex data sources and transforming it into something useful for analysts. 

What you’ll be doing:

• Translate business requirements into well-designed and documented software components based on Microsoft technologies.
• Design, develop and support Commerce projects throughout all phases of the software development life cycle.
• Estimate engineering effort, plan implementation, and rollout system changes. 
• Work with engineers and other cross functional teams like Product Management, Project Management, Release Engineering, Quality Assurance, Operations teams etc. to develop innovative solutions that meet business needs with respect to functionality, performance, scalability, reliability.
• Collaborate with business analysts, application developers and data scientists to implement robust solutions using agile methodologies.
• Understanding functional & non-functional requirements and assisting in analyzing the existing solutions to address day-to-day needs.
• Analyze defects from production/solutions to avoid recurrence of similar defects.
• Find and help fix stability/scalability issues in a very time critical environment.
• Support function rollouts and be available to resolve post-production issues.
• Develop industry and product technical expertise.

 

What you bring to the table:

• Strong problem-solving and analytical skills.
• Excellent communication and teamwork abilities.
• Initiative and accountability in all tasks.

 

What’s needed- Basic/Required Qualifications:

• BA/BS degree in Computer Science, Engineering, or related field or relevant work experience.
• 2+ years of experience in a data engineering role.
• Proficient programming skills in SQL and Python.
• Proficient in RDBMS such as Oracle, SQL Server, NoSQL & Snowflake
• A background in Cloud computing and software engineering (i.e., Azure)
• Experience in Data Pipeline and Workflow Management tools (ETL Tools) (i.e., SSIS, Azure Data Factory, Apache Kafka).

 

What’s needed- Preferred Qualifications:

• 3+ years of experience in managing data pipelines, complex transformations.
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as familiarity with a variety of databases.
• Star schema, Multi -dimensional data sets like Power BI semantic models
• Experience with visualization tools like Power BI and Tableau
• Project management and organizational skills.

 

We Offer:

• Inclusive culture with associate-led Business Resource Groups
• Flexible PTO (22 days) and Holiday Schedule (7 observed paid holidays)
• Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more perks and benefits

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Staples is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, protected veteran status, disability, or any other basis protected by federal, state, or local law.