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The Data Analyst

a.k.a. Reporting Analyst · BI Analyst · Performance Analyst

Turns operational data into actionable visibility.

Department
in the org chart
Setting
Office
behind a desk
Reports to
Operations Manager
one rung up
Typical age
30
median
Data Analyst
Data Analyst
median age 30 · bachelor's degree in business, analytics, or related
composite of operators we work with →

Who they are

Where the data analyst runs the day from the desk.

Turns operational data into actionable visibility.

Software relationship: daily

Goals · what “good” looks like

  • Clear operational visibility teams actually use
  • Earlier problem detection through alerts
  • Better data-driven decision-making
Also called
Reporting AnalystBI AnalystPerformance Analyst
Department
in the org chart
Setting
Office
behind a desk most days

Who shows up · how they think

Demographics & mindset.

Demographics

typical age
30
median we see in the field
schooling
Bachelor's degree in business, analytics, or related
most learned on the job
pay range
$55k – $85k
base + role-tied incentives
software relationship
daily

Typical MBTI types

the temperaments we keep meeting in this seat

INTJ
The Architect
systems thinker
ISTJ
The Inspector
rigorous, by-the-book
INTP
The Logician
untangles tradeoffs

A day with the data analyst

Wake to bed.

11 waypoints. 2 peak-stress hours.

7:00a

Wake

Coffee, dog out, laptop open at the kitchen table. Opens the alerts inbox — first-time-fix dropped 4 points week-over-week on the residential HVAC team, and the dispatch-to-billing reconciliation flagged 23 jobs out of sync overnight.

8:00a

Commute

Train into the office with headphones on. Sketches the FTF investigation in a notebook — by tech, by job type, by day-of-week — before he touches a query. Pen catches the question faster than the keyboard.

8:45a

Anomaly dig

At his desk. Pulls the FTF drop into the warehouse query tool — two new techs onboarded three weeks ago, both running 60-something percent on heat-pump diagnostics. Not a system problem. Not a data problem. A training signal. Writes it up in three bullets before the 10 a.m.

10:00a

Scorecard with the GM

Thirty minutes with the GM and the Service Manager. Walks the FTF drop, the two-tech root cause, and the recommendation — pair them with a senior on heat-pump calls for two weeks. Holds back the seven other dashboards he could show; the room only needs the one decision.

11:00a

Reconciliation cleanup

Back to the 23 out-of-sync jobs. Traces them to a dispatch software update Friday that broke a webhook payload field. Files a ticket with the Systems Administrator with the failing payload attached, then writes a temporary nightly script to backfill until the fix lands. Notes the upstream cause in the data-quality log instead of just patching the symptom.

12:30p

Lunch

Salad bar across the street with a coworker from finance. Twenty minutes, no laptop. Talks about her kid starting kindergarten.

1:00p

Dashboard build

Membership churn dashboard the Sales Manager asked for last week. Three charts, not nine — renewal rate, churn by tenure cohort, churn by service history. Deletes two metrics that were locally interesting but didn't drive a decision. Ships it to a staging link for review.

3:00p

Forecast review

Pairs with the Operations Manager on the Q3 capacity forecast. Walks the call-volume model, the assumptions on the new Installer crew, and the two scenarios — base and aggressive. Logs the assumption changes in the model notes so next quarter's review can see what moved.

4:00p

Data quality routine

Runs the weekly hygiene check. Three job records with negative duration (clock punch errors), eleven with missing zip codes from a CSR shortcut. Pings the CSR with the eleven and the screen path that caused it — the fix is upstream, not in his query.

5:00p

Close-out

Sends the GM the FTF brief in writing so the recommendation lands in the Tuesday huddle. Confirms the membership dashboard review with Sales for tomorrow morning. Reconciliation script queued, data-quality items routed.

9:00p

One last look

Couch, laptop on his knees for ten minutes. Checks the overnight reconciliation ran clean, closes it. Reads twenty pages of a novel before bed.

What they own · where they slip

The job, frankly.

Core duties

what’s on their plate every week

Build dashboards and reports for teams
Define KPI logic and data hygiene standards
Investigate anomalies and performance drivers
Support forecasting and capacity planning
Improve data quality routines

Where they trip

watch for these, they’re common

Building dashboards that track everything
Presenting data without context
Not addressing upstream data quality issues

What makes them a champion

Connect operational data from dispatch, field, billing, and customer systems into one analysis.
, what the data analyst says the first time the dashboard finally clicks.

Career map · the ladder in and out

Where they came from, where they’re headed.

Keep exploring

Other roles in the catalog.