How Tableau Helps You Land a Cloud Engineer Job Faster

# pip install boto3 pandas   Lists EC2 instances into a CSV for Tableau
import boto3
import pandas as pd

ec2 = boto3.client("ec2", region_name="ap-south-1")
rows = []
for page in ec2.get_paginator("describe_instances").paginate():
    for res in page["Reservations"]:
        for inst in res["Instances"]:
            tags = {t["Key"]: t["Value"] for t in inst.get("Tags", [])}
            rows.append({
                "instance_id": inst["InstanceId"],
                "type": inst["InstanceType"],
                "state": inst["State"]["Name"],
                "launch_date": inst["LaunchTime"].date(),
                "team": tags.get("team", "untagged"),
                "env": tags.get("env", "untagged"),
            })
pd.DataFrame(rows).to_csv("inventory_for_tableau.csv", index=False)
print(len(rows), "instances exported")

import pandas as pd

# Stand-in for a billing export (made-up practice values)
raw = pd.DataFrame({
    "usage_date": ["2026-02-01", "2026-02-01", "2026-02-02", "2026-02-02"],
    "product": ["Compute", "Storage", "Compute", "Database"],
    "tag_team": ["web", "", "web", "data"],
    "unblended_cost": ["12.40", "3.10", "13.05", "6.20"],
})
cost = raw.rename(columns={"usage_date": "date", "product": "service", "tag_team": "team", "unblended_cost": "cost"})
cost["date"] = pd.to_datetime(cost["date"])
cost["cost"] = pd.to_numeric(cost["cost"])
cost["team"] = cost["team"].replace("", "untagged")
cost.to_csv("cost_for_tableau.csv", index=False)
print(cost.groupby("team")["cost"].sum())

// Tableau calculated fields for a cost dashboard

// Untagged share of cost
SUM(IF [team] = "untagged" THEN [cost] ELSE 0 END) / SUM([cost])

// Month-over-month change (as a table calculation on SUM([cost]))
(ZN(SUM([cost])) - LOOKUP(ZN(SUM([cost])), -1)) / ABS(LOOKUP(ZN(SUM([cost])), -1))

import pandas as pd

cost = pd.DataFrame({
    "month": ["2026-01", "2026-01", "2026-02", "2026-02"],
    "service": ["Compute", "Storage", "Compute", "Storage"],
    "cost": [300.0, 80.0, 420.0, 82.0],
})
pivot = cost.pivot_table(index="service", columns="month", values="cost", aggfunc="sum")
pivot["change_%"] = ((pivot["2026-02"] - pivot["2026-01"]) / pivot["2026-01"] * 100).round(1)
pivot.reset_index().to_csv("cost_change_for_tableau.csv", index=False)
print(pivot.sort_values("change_%", ascending=False))

import pandas as pd

incidents = pd.DataFrame({
    "opened": pd.to_datetime(["2026-01-04 09:00", "2026-01-22 18:30", "2026-02-11 11:00", "2026-03-02 08:15"]),
    "resolved": pd.to_datetime(["2026-01-04 11:30", "2026-01-22 20:00", "2026-02-11 11:40", "2026-03-02 08:45"]),
    "service": ["web", "api", "web", "api"],
})
incidents["restore_min"] = (incidents["resolved"] - incidents["opened"]).dt.total_seconds() / 60
incidents["month"] = incidents["opened"].dt.to_period("M").astype(str)
monthly = incidents.groupby("month").agg(incidents=("service", "count"), median_restore_min=("restore_min", "median"))
monthly.reset_index().to_csv("reliability_for_tableau.csv", index=False)
print(monthly)

Cloud overview dashboard (one screen):

Top:      Monthly cost | Untagged share | Running instances | Incidents this month
Left:     Cost by service (sorted bars)
Right:    Cost trend (line) with last month for comparison
Bottom:   Resources by environment | Median restore time by month
Filters:  Team, environment, month
Note:     Data sources and refresh date

Project: Cloud cost and reliability dashboard

- Python scripts: inventory export, cost prep, incident prep
- Sample or free-tier data only (no confidential data)
- Tableau dashboard: cost trend, by service, by team, untagged share
- Inventory and reliability pages
- Short write-up: three findings and three actions
- README with architecture of the data flow

Findings (example format):
1. Compute cost rose after new batch jobs; recommend scheduling them off-peak
2. A share of cost is untagged; recommend a tagging policy in Terraform
3. Median restore time fell after adding runbooks; keep updating them

Each finding: chart reference, cause, action, owner

Situation: cost and incident data lived in separate exports
Task: give managers one clear view (practice project)
Action:
  - Python scripts to export and clean data
  - Tableau dashboard with cost, inventory and reliability pages
  - write-up with findings and actions
Result: a one-screen story I can explain in five minutes