Faire · boards.greenhouse.io · checked today
Senior Finance Analytics Engineer
<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Faire</strong></span></p> <p>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.
Skills, with evidence
- Airflow / orchestration
Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with analytics workflow tools (e.g., Airflow, Docker, dbt)
must have - Data modelling
Expert in SQL, with a focus on data modeling, large-scale data processing, and tool development for analytics or financial use cases
must have - Python
Working proficiency in Python (or a similar language) for pipeline development, automation, and tooling
must have - SQL
Expert in SQL, with a focus on data modeling, large-scale data processing, and tool development for analytics or financial use cases
must have - Warehousing
Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with data warehousing (e.g., Snowflake, BigQuery, Redshift, S3)
must have - dbt
Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with analytics workflow tools (e.g., Airflow, Docker, dbt)
must have - Failure handling
<li>Implement and maintain monitoring, alerting, and testing for data workflows to ensure high data quality and reliability, and champion CI/CD and other engineering best practices within the team's pipelines</li>
- AWS
<li>Collaborate closely with Engineering and Data Infrastructure to manage and improve data integrations (e.g., Fivetran connectors, S3 pipelines, and other ingestion patterns) and ensure alignment and efficiency across the organization</li>
not practised here - Data quality
<li>Implement and maintain monitoring, alerting, and testing for data workflows to ensure high data quality and reliability, and champion CI/CD and other engineering best practices within the team's pipelines</li>
- Dashboards & BI
<li>Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with analytics workflow tools (e.g., Airflow, Docker, dbt), data warehousing (e.g., Snowflake, BigQuery, Redshift, S3), and data visualization tools (e.g., Mode, Tableau, Looker)</li>
not practised here - GCP
<li>Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with analytics workflow tools (e.g., Airflow, Docker, dbt), data warehousing (e.g., Snowflake, BigQuery, Redshift, S3), and data visualization tools (e.g., Mode, Tableau, Looker)</li>
not practised here - Governance & security
<li>Experience supporting SOX compliance requirements (e.g., controls, audit trails, change management) in a data or finance systems context</li>
not practised here - Kubernetes
<li>Proven ability to design and maintain production-grade ETL/ELT workflows, including experience with analytics workflow tools (e.g., Airflow, Docker, dbt), data warehousing (e.g., Snowflake, BigQuery, Redshift, S3), and data visualization tools (e.g., Mode, Tableau, Looker)</li>
not practised here - Machine learning
At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL · Data quality
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling · Warehousing · dbt
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Failure handling
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- The source will not let youIntermediate
- Changing a pipeline that’s already runningIntermediate
- SLA-aware alerting flowIntermediate
- Say it out loud≈ 1 h
Not covered by the plan: AWS, Dashboards & BI, GCP, Governance & security, Kubernetes, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Faire closes it
