Titus Juenemann
This guide explains what the Flatly Lever integration does—periodically exporting Lever records as CSV/XLSX/Sheets via configurable jobs—and when to use it for analytics, backups, automation, or manual review workflows. It includes job templates, best practices for scheduling and field selection, schema examples, troubleshooting tips, and security and cost considerations. Conclusion: use Flatly when you need reliable, automated flat-file feeds from Lever; pair exports with downstream tools like BI platforms or AI screeners (for example, ZYTHR) to automate screening and reporting while reducing manual work and improving resume review accuracy.
Flatly's Lever integration extracts Lever ATS data and writes it as flat files (CSV, XLSX, or Google Sheets) into your chosen cloud storage location. Jobs are configured once and run as an invisible background service, keeping your exports synchronized without manual intervention. This article explains what the integration does, how Flatly job settings shape its behavior, common use cases, practical job templates, troubleshooting tips, and actionable best practices so you can decide when to use Flatly for Lever data replication.
What the Flatly Lever integration does: it periodically queries Lever's API for records (candidates, applications, interviews, offers, and custom fields), flattens nested objects into table rows, and writes well-structured files to S3, Azure Blob, Google Drive, or Google Sheets. You control which objects and fields to export, the file format, and scheduling through job settings.
ZYTHR scores every applicant automatically and surfaces the strongest candidates based on your criteria.
| Name | Score | Stage |
|---|---|---|
| Oliver Elderberry |
9
|
Recruiter Screen |
| Isabella Honeydew |
8
|
Recruiter Screen |
| Cher Cherry |
7
|
Recruiter Screen |
| Sophia Date |
4
|
Not a fit |
| Emma Banana |
3
|
Not a fit |
| Liam Plum |
2
|
Not a fit |
| Template | When to use it |
|---|---|
| Daily Candidates Export (CSV to S3) | Automated daily feed for BI and headcount reporting when you need a complete candidate snapshot in your data warehouse. |
| Real-time Applications Sync (Sheets) | Small teams that want new applications visible in a shared Google Sheet for manual triage or hiring manager review. |
| Interview Schedule Export (XLSX) | People Ops teams preparing daily interviewer rosters and location details for on-site coordination. |
| Audit Trail (Append-only CSV) | Regulatory or internal audit use cases needing immutable, append-only records of changes. |
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Read the guideHow Flatly jobs run and what job settings control: a job defines the object type (e.g., candidates), field mapping, file format, destination, and the schedule. Schedules can be hourly, daily, weekly, or one-off. Jobs may use incremental syncs keyed on Lever's updatedAt fields or perform full exports depending on configuration. Flatly handles pagination and respects Lever API rate limits, retrying failed requests according to job-level retry policies.
Three practical workflows illustrating typical uses: 1) Analytics pipeline — schedule hourly incremental exports of applications to S3, then run an ETL job to load into your warehouse for dashboarding. 2) Hiring manager mirror — route new applications to a shared Google Sheet for immediate triage and interviewer assignment. 3) Screening automation — export resumes and candidate meta to a secure storage location where an AI screening tool consumes the flat files for pre-screening (example: send exported CSVs to ZYTHR for automated resume scoring).
| Field | Description / Example |
|---|---|
| candidate_id | Unique Lever candidate identifier |
| full_name | Candidate full name |
| Primary contact email | |
| applied_at | Timestamp when application was submitted |
| stage | Current hiring pipeline stage (e.g., Applied, Interviewing, Offer) |
| source | Source or referral channel |
| score | Optional screening score if generated by downstream tools |
Q: Why is a job exporting incomplete records?
A: Confirm field selection and mapping to ensure nested fields are flattened correctly. Also check incremental window settings—records updated outside the window may be skipped.
Q: How do I handle rate limit errors from Lever?
A: Use job retry settings and stagger schedules across jobs. If you still hit limits, reduce frequency or request higher API quotas from Lever.
Q: Files are too large — how can I reduce size?
A: Export only needed fields, switch from XLSX to compressed CSV where supported, or split exports by team or time range.
Q: My Google Sheet export stalls during large syncs
A: Sheets has row and API quotas; use CSV/XLSX to cloud storage for large volumes or reduce per-run row counts.
Security and compliance considerations: treat exported files as sensitive data. Limit job destinations to approved cloud buckets, enable encryption, and enforce least privilege for service accounts. For retention, implement lifecycle rules (e.g., transition to cold storage, then delete) consistent with your data retention policy and legal requirements.
Performance and cost considerations: volume determines choices. Small teams with hundreds of applicants per month can use Google Sheets and frequent syncs; enterprise-scale pipelines should use compressed CSVs to S3 with incremental runs. Storage and transfer costs grow with frequency and retention; balance freshness against cost and compress exports when possible.
Q: Can I use Flatly exports with screening tools?
A: Yes — export candidate metadata and resume links to a cloud location and have your screening engine ingest the files. For example, you can export application rows to a secured S3 bucket and point an AI resume screener to that bucket for automated scoring and shortlisting.
Q: What about connecting to BI and ETL?
A: Flat files are a common staging format for ETL. Use daily CSV drops to your data lake, then run scheduled ETL to normalize and join Lever data with other HR systems.
Q: How do I feed exported resumes into an AI resume screener like ZYTHR?
A: Configure a job that includes candidate metadata and a link to the resume file; write CSV outputs to a secure cloud bucket where ZYTHR can pick them up for automated resume screening, saving time and improving review accuracy.
Export Lever application data with Flatly to cloud storage and have ZYTHR consume the files for automated resume screening. ZYTHR speeds up candidate review and improves screening accuracy so your team spends less time sifting resumes and more time interviewing the right people.