7 days old

Data and Analytics Manager

New York, New York 10016
  • Job Type
  • Job Status
    Full Time

Born out of frustration with the traditional cash register business, ShopKeep was designed by a retailer with a noble aim: to rescue independent business owners from the nightmare of archaic point of sale systems, and replace them with something beautiful, simple, and affordable. It turned out that by doing this, we were giving our fellow merchants a fighting chance against the big guys.

Today, our mission is simple: empower independent business owners to dream big and to fight smart. We’re doing this through our cloud-based architecture, amazing customer care, and intuitive software that delivers the data small business owners need to run smarter businesses.

At ShopKeep, we’ve been successful because of our awesome team that believes small businesses make up the heart of their communities.


About This Role

You will lead our data team. We seek a hands-on technical manager who wants to take the next step in a career of building data tools for better decision-making and improve the overall data experience at ShopKeep. We are part of Engineering and work closely with Product, and every other department in the company.

We have a robust infrastructure, terabytes of structured data, and a well-organized Looker installation. Our data culture across the company is good and always improving, but our roadmap is far from complete. We’re ready to take it up to the next level with someone who can lead a small team in the vision of implementing data self-service across all departments of the company.

You should be comfortable with:

  • Advocating for forward-looking data practices in a room of business leaders
  • Performance tuning databases as well as improving data architectures
  • Coaching
  • Writing Python and debugging ETL
  • Taking an abstract business question and designing a KPI from ETL to dashboard

What You’ll Do

  • Lead: The team that owns data analysis at Shopkeep needs a leader who has seen some failures and has opinions about the right way to build analytics products.
  • Manage: Be responsible for the growth and happiness of your team. Influence the future direction of database usage for all engineering teams at ShopKeep.
  • Respond: All of our teams are hungry for insights. You’ll sit down with stakeholders and field abstract requests. This may be pointing someone to the answer, teaching them how to fish, or ticketing a large project for later.
  • Teach: Data self-service is a continuous process for the architects just as much as for the consumers.
  • Scale: Eventually every system is outgrown.  Help us design and build for our future.
  • Design: Thoughtful architecture and careful naming is how we maintain steady delivery.
  • Own: Sometimes things break, and every system needs owners.
  • Learn: Working in technology at Shopkeep means being exposed to new things, so expect to be challenged.



We provide the essentials...

  • Medical, Dental, and Vision Insurance
  • Unlimited Vacation Days
  • Commuter Benefits
  • 401k Matching

...and the fun-damentals:

  • Catered Meals
  • Happy Hours
  • Beer Fridge
  • Stocked Snack Shelf
  • Hackathons

Still have energy to burn?

  • Meditation Sessions
  • Massage Therapy
  • Standing Desks


ShopKeep is an Equal Opportunity Employer.


What We’d Like to See


  • Ability to lead from the front - knowing how to be the role model for the team.
  • Ability to coach software developers on database best practices.
  • Excellent communication skills and desire to communicate effectively.
  • Interest in technology and eagerness to learn.

Required Technical Skills

  • Strong SQL skills (3-5 years)
  • Comfort with ETL and Python (3-5 years)
  • Deep understanding of PostgreSQL and large relational data store management
  • Experience with Amazon Web Services - RDS

Desired Technical Skills

  • Self-service BI tools, especially Looker
  • Test-driven development
  • Statistics (academic or business)
  • Supervised and unsupervised machine learning
  • Time-series analysis (e.g. ARIMA)
  • Productionizing data science workflows
  • Non-relational data stores and messaging infrastructure: ElasticSearch, Redis, Kafka, etc



  • Data Analytics
  • Data Engineer
  • Business Intelligence

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