Faire · boards.greenhouse.io · checked today
Senior 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
- Python
Production-level development experience in Python.
must have - SQL
Strong SQL skills with demonstrable competencies in designing well-architected data models and optimizing query performance.
must have - Warehousing
Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
must have - Airflow / orchestration
Experience with Airflow, Docker, DBT or similar analytics workflow tools.
- Data modelling
<li>Lead the technical execution in building scalable data models and pipelines for a domain defining the architecture, feature requirements, roadmap.</li>
- Data quality
<li>Build data pipelines with rigorous approaches to data quality, validation, testing, and observability.</li>
- Cost & performance
<li>Optimize processes for operational excellence in project management and system reliability.</li>
- dbt
<li>Experience with Airflow, Docker, DBT or similar analytics workflow tools.</li>
- Kubernetes
<li>Experience with Airflow, Docker, DBT or similar analytics workflow tools.</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
Warehousing · Data modelling · 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
- 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
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- A filter after a window function: pushed down or not?Advanced
- Say it out loud≈ 1 h
25 drills · Intermediate + Advanced≈ 14 hours
Not covered by the plan: Kubernetes, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 13 dremoved the moment Faire closes it