Job description

About Faire Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours. About this role: Our Engineering organization owns the software that makes our marketplace work. The Data Platform group supports everyone at Faire who depends on data: Product Engineering, Data Science, Machine Learning, Analytics, Strategy, Finance, and Product. Our job is to make sure the data is there, it's right, and people can find it and query it without having to think about the plumbing underneath. We are hiring a Staff Engineer to own that plumbing. Concretely, this means the path data takes out of our production databases (CockroachDB and MySQL) and into a place where analysts and data scientists can query it. Today that involves Fivetran, Kafka, Spark, and Airflow landing data in Snowflake and Databricks. It works, but it grew up over time and it shows. We want someone who can design the next version of it, build the hard parts personally, and bring the rest of the company along. This is a hands-on role. You'll also be the person other teams come to when they need to know how data should move at Faire. What you'll do: Set the technical direction for how data moves from production systems into our analytical stores, and own the roadmap to get there over the next couple of years. Build the CDC and streaming ingestion layer: CockroachDB changefeeds and MySQL binlogs into Kafka, then into Iceberg tables on S3. You'll be responsible for the hard details like ordering, deduplication, late data, schema changes, and backfills. Implement data contracts and quality checks throughout our platform Put real ownership and SLAs on the datasets the business runs on, and wire quality checks into the platform with tools like Anomalo and Monte Carlo so we hear about broken data before a dashboard or a model does. Run Airflow and Fivetran well, and have an opinion about what we should keep buying versus what we should build. Own reliability for the platform: SLOs, on-call, incident reviews, and the follow-through so the same thing doesn't break twice. Work with the senior engineers, data scientists, and analysts who depend on this platform, and lead the migration of existing pipelines onto the new one without breaking what they rely on. Mentor the engineers around you. We want the team's data engineering practice to be better because you were here. Qualifications: You've built and run data infrastructure that other teams depended on, at meaningful scale, and you've been the person setting direction for it, not just working on it. Deep experience with change data capture and streaming ingestion from operational databases through Kafka. You know what goes wrong with ordering, duplicates, snapshots, and schema evolution because you've dealt with it. Hands-on experience with lakehouse architectures on an open table format. Iceberg on S3 is what we use, so that's especially valuable. You should be comfortable talking about partitioning, compaction, catalogs, and copy-on-write versus merge-on-read. Strong Spark skills, and experience running Databricks and Snowflake against shared storage. Experience with data quality and observability in practice, including data contracts, SLAs, and tools like Anomalo or Monte Carlo. Experience operating Airflow at scale and working with managed ingestion like Fivetran. Strong SQL, and good instincts for how to model data so analysts and data scientists can actually use it. Solid Python plus at least one of Kotlin, Java, Scala, or Go. Experience shipping infrastructure on AWS with Terraform. A working understanding of data governance: access control, PII, retention and deletion, lineage, and audit. A track record of leading cross-team data initiatives and migrations, and of mentoring senior engineers. You can explain a technical tradeoff to a leadership team and to a new grad, and you can get people who disagree with each other to a decision. You take ownership of things that are broken or unowned, and you're willing to be on call for the systems you build. Experience in a marketplace, e-commerce, or other transaction-heavy business is a plus. Technologies we use and teach: Python, Kotlin, SQL Kafka, Fivetran, Airflow S3, Apache Iceberg, Snowflake, Databricks, Apache Spark AWS, Terraform, Kubernetes CockroachDB, MySQL, Scylla and DynamoDB Salary range: San Francisco & New York: the pay range for this role is $246,500 to $339,000 per year. This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future. Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting. Why you’ll love working at Faire Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly. Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day. Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours. Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work. Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success. Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog . Faire 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 or gender expression. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form ( bit.ly) Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (faire.com)

Requirements

Must-haves

Set the technical direction for how data moves from production systems into our analytical stores, and own the roadmap to get there over the next couple of years.
Implement data contracts and quality checks throughout our platform
Put real ownership and SLAs on the datasets the business runs on, and wire quality checks into the platform with tools like Anomalo and Monte Carlo so we hear about broken data before a dashboard or a model does.
Run Airflow and Fivetran well, and have an opinion about what we should keep buying versus what we should build.
Own reliability for the platform: SLOs, on-call, incident reviews, and the follow-through so the same thing doesn't break twice.
Work with the senior engineers, data scientists, and analysts who depend on this platform, and lead the migration of existing pipelines onto the new one without breaking what they rely on.
Mentor the engineers around you. We want the team's data engineering practice to be better because you were here.
You've built and run data infrastructure that other teams depended on, at meaningful scale, and you've been the person setting direction for it, not just working on it.
Deep experience with change data capture and streaming ingestion from operational databases through Kafka. You know what goes wrong with ordering, duplicates, snapshots, and schema evolution because you've dealt with it.
Strong Spark skills, and experience running Databricks and Snowflake against shared storage.
Experience with data quality and observability in practice, including data contracts, SLAs, and tools like Anomalo or Monte Carlo.
Experience operating Airflow at scale and working with managed ingestion like Fivetran.
Strong SQL, and good instincts for how to model data so analysts and data scientists can actually use it.
Solid Python plus at least one of Kotlin, Java, Scala, or Go. Experience shipping infrastructure on AWS with Terraform.
A working understanding of data governance: access control, PII, retention and deletion, lineage, and audit.
A track record of leading cross-team data initiatives and migrations, and of mentoring senior engineers.
You can explain a technical tradeoff to a leadership team and to a new grad, and you can get people who disagree with each other to a decision.
You take ownership of things that are broken or unowned, and you're willing to be on call for the systems you build.
Python, Kotlin, SQL
Kafka, Fivetran, Airflow
S3, Apache Iceberg, Snowflake, Databricks, Apache Spark
AWS, Terraform, Kubernetes
CockroachDB, MySQL, Scylla and DynamoDB

Nice-to-haves

Experience in a marketplace, e-commerce, or other transaction-heavy business is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningrestsqlpythonkotlinjavagoawskubernetesaiaccessibility

Platform: greenhouse
Effort: quick