Data Entry Virtual Assistant (SaaS Platform) — Excel + Pivot Tables + METRC Experience

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TYPE OF WORK

Full Time

SALARY

$4.50/hour

HOURS PER WEEK

40

DATE UPDATED

Feb 17, 2026

JOB OVERVIEW

Position: Virtual Assistant (Data Entry / SaaS Operations Support)
Location: Remote (Philippines preferred — OnlineJobs.ph)
Hours: Full-time, 40 hours/week
Coverage Window: Monday–Saturday, 6:30 AM–7:30 PM Pacific Time (team-based shift coverage)
Shift Model: You’ll be assigned a consistent shift within the coverage window and coordinate handoffs with the team
Pay: $[4.50/hr] (more based on experience) + paid interview (includes Excel/pivot exercise)

About the Role

We’re hiring a detail-oriented Virtual Assistant to support data entry and data quality tasks inside our SaaS platform. You’ll join an experienced operations team that’s been doing this work for 3 years, with defined SOPs, QA checks, and shift handoffs to maintain coverage throughout the day.

This role requires strong Excel skills (including pivot tables) and comfort working in structured workflows. METRC experience is a major plus—ideal candidates can demonstrate familiarity with the platform and provide proof of credentials (details below).

Responsibilities

Enter and maintain accurate records in our SaaS platform

Validate data quality: completeness, formatting, duplicates, and exceptions

Use Excel daily for cleaning and preparation (filters, sorting, text functions, data validation, basic formulas)

Build pivot tables to summarize datasets and support QA (counts, totals, grouping, segmentation)

Follow documented SOPs and naming conventions precisely

Maintain clear shift notes and handoff summaries to the next tea ---------- mber

Flag anomalies and edge cases promptly with clear context/screenshots

Requirements (Must-Have)

Excel proficiency, including pivot tables (you will demonstrate this during a paid interview)

Proven accuracy with repetitive, detail-heavy work

Strong written English and ability to follow instructions exactly

Reliable internet and consistent availability for a set schedule within the coverage window

Comfortable working in web apps (SaaS tools, dashboards, Google Drive)

Strong Preference / Bonus

METRC experience (cannabis track-and-trace)

Ability to show METRC credentials or verifiable experience (see note below)

Prior experience with CRM/ERP-like systems, data cleanup, and record reconciliation

Familiarity with CSV import/export workflows and data normalization

Paid Interview Skill Check (Excel)

During the paid interview, you will:

Open a provided dataset

Build a pivot table to answer specific questions (e.g., totals by category, counts by status, grouping by date)

Share your screen and explain your approach briefly

METRC Credentials (Verification)

If you have METRC experience, include one of the following:

A screenshot showing your name/email in a METRC-related user/account context with sensitive details redacted

A brief description of your METRC role (facility type, tasks performed, duration, state)

Any training/certification or proof of access you can share safely

(Do not send API keys, passwords, or any sensitive login details.)

How to Apply (Required)

Please reply with:

Full name + location (city/province)

Availability: confirm you can work 40 hrs/week and specify your preferred shift hours in Pacific Time

Excel skill level (beginner/intermediate/advanced) + 1–2 examples of pivot tables you’ve built

METRC experience: yes/no. If yes, describe your experience and include your verification method (redacted screenshot or details)

Typing speed (WPM) + internet speed test result (screenshot ok)

Answer these questions:

What’s the difference between a pivot table and a normal filtered table?

When a pivot table shows wrong totals, what are two things you check first?

Subject line: VA Data Entry + Excel Pivot + METRC – [Your Name]

What Success Looks Like (First 30 Days)

You can complete data entry tasks with minimal errors and follow SOPs reliably

You consistently produce clean handoffs between shifts

You can identify patterns, duplicates, and anomalies before they become downstream issues

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