"Why are we losing engineers after their second year?" A VP of engineering asks it in a staff meeting, and someone forwards it to you with a short note: Can you look into this? Answering that kind of question honestly is the whole job behind these remote people analytics analyst jobs. The position is full-time, open to applicants worldwide, and pays USD 75,000 per year. Before you write a single query, though, you have to decide what "losing" means.
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Start with the definition, not the data
The VP said "losing." The HRIS says "termination," and it says it for many reasons at once. A contractor whose contract ended is a termination. So is someone moved to a sister entity, someone laid off in a restructure, and someone who resigned to join a competitor. Only the last one is what the VP is worried about.
So the opening task is a written metric definition, agreed with an HR business partner before anything is built. For this question, it might read: voluntary attrition is the count of regular, non-contract employees in engineering job families who left by their own choice during a period, divided by the average headcount of that same group across the period. Then settle the edge cases. Does an internal transfer out of engineering count? (Usually no; it is mobility, not loss.) Is "second year" tenure measured from the original hire date or from a rehire date? What happens to people on long leave?
Headcount needs the same care. Point-in-time headcount on the last day of a month will differ from average headcount, and both differ from FTE. Pick one, write it down, and put that written note on the dashboard itself so nobody reads a 14% rate against a figure built differently.
Pulling and joining the HRIS records
With definitions settled, you go to the data. Most of it lives in the HRIS as effective-dated records: a worker table, a job history table where every promotion, manager change, or location move adds a row, and a terminations table with reason codes. You will write SQL that rebuilds each person's state on any given date, which usually means a window function or a join on effective date ranges rather than a simple lookup.
The cleaning takes most of the hours. Reason codes get retired and replaced, so "Resigned - Better Opportunity" from three years ago and "Voluntary - Career" today need to map to one category. Rehires show up with two IDs in some systems. Managers change mid-month. Keep a small, versioned mapping table for these fixes rather than burying them in one long query.
Privacy rules and small groups
If a dashboard shows that one person on a five-person team left last quarter, everybody on that team knows exactly who it was. If it shows engagement scores for a group of three, the "anonymous" survey is not anonymous. People analytics teams handle this with minimum group sizes: any cut of the data below an agreed threshold is suppressed or rolled up into a larger group. The organization sets the number; you enforce it in every filter and export.
Remote work adds its own layer. Common setups include role-based access in the HRIS, a VPN or managed device, a ban on saving row-level employee files to personal machines, and logs of who viewed what. Rules vary by country and employer, so follow the policy you are given and ask when something is unclear.
Exploring the question in Python
Once the base table is clean, the analysis itself often moves into Python with pandas. For the engineering question, a survival curve (Kaplan-Meier, for example) shows when people leave far better than a single annual rate does. In a made-up example, the curve might drop sharply between months 20 and 28, which is the window the VP was asking about.
Then you compare who left in that window against who stayed: time since last promotion, manager changes, pay position within band, last engagement score. A logistic regression or simple rate comparison can point at candidate drivers, and you check whether each is real or small-sample noise.
The dashboard HR leaders will open every month
The one-off answer matters, but the lasting output is usually a dashboard. Building attrition and headcount dashboards for HR leaders sits at the center of the role, done in Tableau or in Visier, a workforce analytics tool where many standard HR measures are already set up. Some organizations also use Workday Prism Analytics when they want to combine non-Workday data with Workday records for reporting. You do not need all three; the listing asks for strong Tableau skills, and Visier or Prism experience counts as a bonus.
A practical layout: headcount trend at the top, voluntary and involuntary attrition split out below, tenure-band breakdown, and filters for department and region that respect the suppression threshold.
Telling the story without overclaiming
Here is where judgement counts. Your analysis might show that engineers who had no promotion by month 24 left at twice the rate of those who did. It is tempting to tell the VP that slow promotion causes attrition. You cannot say that from observational HR data. Teams with weak managers might both promote less and lose more people.
So you present it carefully: here is the pattern, here is how big it is, here is what it does not prove, and here is a cheap way to learn more, such as stay interviews or a closer look at one team. If the honest answer is "we don't know yet," say so and explain what would settle it.
Four duties in the people analytics analyst job description
- Build attrition, headcount and hiring dashboards in Tableau or Visier.
- Clean and join HRIS data using SQL and Python.
- Analyze engagement and turnover drivers.
- Present findings to HR business partners and leaders.
An HR data analyst role usually centers on reporting accuracy and data audits; this role spends more time on open questions and advising leaders. Titles overlap a lot.
Degrees and tools for remote people analytics analyst jobs
The required degree is a bachelor's in statistics, psychology, HR, or data analytics, plus 2 to 4 years of analytics work, ideally on HR data. Key people analytics analyst skills include strong SQL, Tableau, and Python, with Visier or Workday Prism as a plus. AIHR certificates help.
How to become a people analytics analyst depends on where you start: from HR, the gap is usually SQL and statistics; from data analysis, it is HR knowledge such as reason codes and job architecture. Entry-level remote people analytics analyst jobs exist but are less common, since this role wants two years of analytics work; for people analytics analyst jobs with no experience, an internal HR reporting or HR operations role is a common first step.
Stated salary for this role
The people analytics analyst salary here is USD 75,000 per year, full-time. This figure applies to this opening only. People analytics analyst salary per year elsewhere depends on the employer and the scope of the work.
Portfolio project on synthetic data
The best preparation is a portfolio project built on synthetic data. Generate a fake company of a few thousand employees in Python, with some realistic mess such as retired reason codes and a rehire. Then do the whole cycle: write a one-page metric definition, build the SQL or pandas logic, apply a suppression rule, build a Tableau dashboard, and write a short memo that is careful about causation. Never use real employee data from a past job, even anonymized.
A people analytics analyst course adds structure, and an AIHR people analytics analyst certification can help; general SQL courses cover the technical side. Your people analytics analyst resume should link the project and name the metrics you defined, not just the tools you used.
Submitting the CV and project link
Submit your CV and project link through this page. Openings for people analytics analyst jobs remote worldwide draw applicants from many countries, so a clear project helps. When comparing people analytics analyst work-from-home jobs, read each listing closely to see what data and system access it requires. You won't be asked to pay to apply, and an employer that wants payment from applicants isn't genuine.
