Staff Software Engineer - Core Data Engineering

Remote, San Francisco, California, United States

Applications have closed

Afresh

Afresh is the world’s leading fresh technology company helping grocers make smarter decisions throughout their fresh supply chain, ultimately increasing profits, efficiency, and significantly reducing food waste.

View company page

Afresh is on a mission to eliminate food waste and make fresh food accessible to all. Our first A.I.-powered solution optimizes ordering, forecasting, and store operations for fresh food departments in brick-and-mortar grocers. With our Fresh Operating System, regional and national grocery retailers have placed $1.6 billion in produce orders across the US and we've helped our partners prevent 34 million pounds of food from going to waste. Working at Afresh represents a one-of-a-kind opportunity to have massive social impact at scale by leveraging uncommonly impactful software – we hope you'll join us!

About the role

You’ll be a key member of our Core Data Engineering team. You’ll be owning changes to our common code and standard schemas to fit the company’s product needs (i.e. new products and features), and creating tools to help increase our customer integration velocity. If you're interested in building software to support our mission, we hope you'll join us!

  • Design, build, scale, and deploy fast and reliable ETL pipelines in Python, Pandas, Spark, SQL, and DBT, - across our data platforms (powered by Databricks, Postgres, Snowflake, and Delta Lakes) - to process billions of data points from US retail stores and to power our recommendation engine and ordering system
  • Collaborate with an interdisciplinary team of experts in Go-To-Market, machine learning, data science, product, and product engineering to understand how to properly transform customer data and to implement solutions
  • Monitor and analyze data, and work with internal stakeholders to either create dashboards, clearly escalate high-priority issues back to the customer, or find novel workarounds to extract the signal we need from customer data
  • Develop standard tools that help increase the velocity at which we can integrate new customers and release new product features

Skills and experience

  • Ability to identify a problem or area of improvement, design, solution, and see it through to implementation
  • Strong understanding and experience with various data stores (databases, data warehouses, key/value stores, etc.). Experience with Apache Spark, Pandas, SQL, or other Big Data frameworks and cloud infrastructure preferred
  • Experienced with architecture & design of data driven solutions
  • Strong problem-solving ability and ability to work through ambiguity and incomplete specifications
  • Dedication to code quality, testing, design processes, automation, and operational excellence
  • An active stakeholder in engineering tools discussions and help us build and design for future growth
  • Excellent written communication, verbal communication, and collaboration skills

The following represent the skills and experience our ideal candidate possesses. We encourage candidates to apply, even if they do not fulfill all the listed criteria.

Afresh is committed to pay equity and providing highly competitive cash compensation, equity, and benefits package. Afresh conducts a pay equity audit twice each year to ensure that jobs of similar scope and impact are paid similar amounts. The final compensation offered for this role will be based on multiple factors such as the role’s scope, complexity, internal equity, the candidate’s experience/expertise, and success through the interview process.

Salary Band:


#LI-REMOTE

About Afresh

Founded in 2017, Afresh is working on the #1 solution to curb climate change: reducing food waste. By combining human insight and transformative technology, we're helping grocers provide fresher food to customers at more affordable prices.

Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals including ICML, and we've raised over $148 million in funding from investors including former co-CEO of Whole Foods Market Walter Robb and Eric Schmidt's Innovation Endeavors.

Fresh is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.

Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.

Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, WI. However, there may be key roles that will require a candidate/employee to be local to our San Francisco, CA office. In which case this requirement will be included in the job posting details under "Skills and experience" for reference. 

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Architecture Big Data Databricks Engineering ETL ICML Machine Learning Pandas Pipelines PostgreSQL Python Research Snowflake Spark SQL Testing

Perks/benefits: Career development Competitive pay Equity Startup environment

Regions: Remote/Anywhere North America
Country: United States
Job stats:  11  1  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.