Data Engineer, Marketing

  • San Francisco, CA
  • Employees can work remotely
  • Full-time
  • Alternate Location: New York, United States
  • Position open to remote: Yes

Company Description

Square builds common business tools in unconventional ways so more people can start, run, and grow their businesses. When Square started, it was difficult and expensive (or just plain impossible) for some businesses to take credit cards. Square made credit card payments possible for all by turning a mobile phone into a credit card reader. Since then Square has been building an entire business toolkit of both hardware and software products including Square Capital, Square Terminal, Square Payroll, and more. We’re working to find new and better ways to help businesses succeed on their own terms—and we’re looking for people like you to help shape tomorrow at Square.

Job Description

As a Data Engineer on the Marketing team, you will join an organization whose mandate is to develop foundational data and reporting infrastructure to driving Square's revenue growth and accelerating seller acquisition. You will collaborate and work with teams across Square to build outstanding data pipelines, dashboards and processes that stitch together complex sets of data stores and guide large investment decisions. Your work will have an impact on hundreds of partners at Square.

You Will:

  • Develop data foundation and architecture, data pipelines and dashboards to ensure accurate and reliable business reporting
  • Partner with business leads to understand their data and reporting requirements and translate them into Product Requirement Definitions and technical specifications
  • Be the expert on end-to-end data flow for Marketing
  • Bridge the gap between business requirements and technical implementation by troubleshooting data discrepancies and implementing scalable solutions, and communicate with high-level stakeholders in formats
  • Monitor daily execution, diagnose and log issues, and fix business pipelines to ensure SLAs are met with internal stakeholders
  • Make data model and ETL code improvements to improve pipeline efficiency and data quality
  • Mentor Data Engineers and Data Analysts, and promote data engineering best practices

Qualifications

You Have:

  • 6 years experience in Data Engineering or similar role
  • 6 years experience in writing complex SQL and ETL development with experience processing extremely large datasets within cloud-based data warehouses like Snowflake, Google BigQuery, and Amazon Redshift
  • Expert knowledge in data warehousing architecture and concepts, and dimensional data modeling
  • Experience in Python
  • Experience working with business teams on complex problems and translating them to efficient, scalable and easy to maintain data engineering solutions
  • BS degree in Engineering, Computer Science, Math or a related technical field

Technologies we use and teach:

  • SQL and Python
  • Looker, or other data visualizations technologies
  • ETL scheduling technologies with dependency checking such as Airflow
  • Linux/OSX command line, version control software (git)

Additional Information

At Square, we value diversity and always treat all employees and job applicants based on merit, qualifications, competence, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us by sending an email to assistance(at)squareup.com. We will treat your request as confidentially as possible. In your email, please include your name and preferred method of contact, and we will respond as soon as possible.

Perks

At Square, we want you to be well and thrive. Our global benefits package includes:
  • Healthcare coverage
  • Retirement Plans
  • Employee Stock Purchase Program
  • Wellness perks
  • Paid parental leave
  • Paid time off
  • Learning and Development resources