SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: CAD 216,000 - 297,000 / annual
Faire is a technology wholesale platform connecting independent retailers globally with suppliers. The Data Platform group supports product engineering, data science, machine learning, analytics, strategy, finance, and product teams by ensuring data is accurate, accessible, and queryable without requiring deep infrastructure knowledge.
You will own the data infrastructure that moves data from production databases (CockroachDB and MySQL) into analytical stores (Snowflake and Databricks). The current system uses Fivetran, Kafka, Spark, and Airflow but has accumulated technical debt. You will design and build the next generation of this platform, handling the complex details personally while bringing the rest of the company along.
Key responsibilities include:
- Setting technical direction for data movement from production to analytical systems and owning the multi-year roadmap
- Building CDC and streaming ingestion layers: CockroachDB changefeeds and MySQL binlogs into Kafka, then into Iceberg tables on S3, managing ordering, deduplication, late data, schema changes, and backfills
- Implementing data contracts and quality checks throughout the platform
- Establishing ownership and SLAs on critical business datasets, integrating quality checks with tools like Anomalo and Monte Carlo
- Operating Airflow and Fivetran at scale, with informed opinions on build-versus-buy decisions
- Owning platform reliability: SLOs, on-call rotations, incident reviews, and preventing recurrence
- Leading cross-team pipeline migrations to the new platform without disruption
- Mentoring engineers to improve the team's data engineering practice
This is a hands-on role where you will be the technical authority other teams consult for data movement strategy at Faire.
Hybrid work: 3 days per week in office (Tuesdays, Thursdays, plus one flex day), with up to 4 weeks remote flexibility annually.
QUALIFICATIONS:
- Proven track record building and running data infrastructure at meaningful scale that other teams depended on, with responsibility for setting technical direction
- Deep hands-on experience with change data capture and streaming ingestion from operational databases via Kafka, including practical knowledge of ordering, deduplication, snapshots, and schema evolution
- Hands-on experience with lakehouse architectures using open table formats; Iceberg on S3 especially valuable. Comfortable discussing partitioning, compaction, catalogs, and copy-on-write vs. merge-on-read tradeoffs
- Strong Spark skills and experience running Databricks and Snowflake against shared storage
- Practical experience with data quality and observability, including data contracts, SLAs, and tools like Anomalo or Monte Carlo
- Experience operating Airflow at scale and working with managed ingestion tools like Fivetran
- Strong SQL and good instincts for data modeling that serves analysts and data scientists
- Solid Python plus at least one of Kotlin, Java, Scala, or Go; experience shipping infrastructure on AWS with Terraform
- Working understanding of data governance: access control, PII, retention and deletion, lineage, and audit
- Track record leading cross-team data initiatives and migrations, mentoring senior engineers
- Ability to explain technical tradeoffs to both leadership and junior engineers; skill in building consensus among disagreeing stakeholders
- Ownership mindset for broken or unowned systems; willingness to be on-call for built systems
- Experience in marketplace, e-commerce, or transaction-heavy business is a plus
