◆ Data & Analytics

What a data visualization specialist
really does.

22 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.

22evidenced tasks
67,140in the US (2025)
$139,500median pay / year
10systems it runs on
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Monitor data warehouse health
Monitor the data warehouse health for the nightly ingestion pipeline, …2 sources agree

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The work, task by task

22 tasks
Hands on the work17
Model data+
Model the customer engagement dataset into a star schema, define facts and dimensions, document column meanings and grain, create example SQL for joins and aggregations, and circulate the model to analytics and product for review on Wednesday.
escojd2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Troubleshoot data issues+
Troubleshoot today's dashboard discrepancies by tracing the metric from the visualization back to its source table, validate source row counts and data quality checks, isolate whether the issue is in the transformation or freshness, and note remediation steps in the incident log.
escojd2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Implement ETL processes+
Design and deploy nightly ETL that extracts raw event batches, applies cleansing rules and validation, writes partitioned cleaned tables with data quality reports, and notify the data stewards on failure starting next Monday.
jdonet2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Perform system analysis, data analysis or programming, using a variety of computer languages and procedures.+
Analyze the data pipeline to find where schema drift and null inflation occur, profile sample tables and scripts, list the failing transformations with line numbers and propose three remediation options with estimated effort and risk by Tuesday.
onet
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Create or implement metadata processes and frameworks.+
Design a metadata framework that captures source lineage, field definitions, retention policy and RDF-compatible identifiers, produce the metadata model diagram and a one-page governance checklist for the analytics team by Friday.
onet
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Configure database software+
Harden the analytics database configuration for performance: tune connection limits, storage compaction, backup schedule and retention, record the changes and rollback steps, and validate with a full ingest test by Wednesday.
jd
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Create data marts+
Build the sales data mart with cleansed source feeds, standardized field mappings, primary keys, partitioning strategy and RDF-friendly identifiers, populate with a month of data and deliver a refresh script and documentation by next Monday.
jd
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Maintain data security+
Lock down the visualization project datasets and access lists, revoke any users without a business justification, encrypt the archive copy, and document the changed permissions and retention policy for audit by Friday.
jd
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Watch and assess3
Grow the practice1
Keep it safe1

What the work runs on

named inside the evidenced tasks
6 tasksApache Sparkused to run diagnostic transformations and row-level checks at scale to isolate data issues
4 tasksApache Hadoopprovides the storage and batch processing framework where onboarding and lifecycle procedures are applied
3 tasksAmazon Redshifthosts and queries the analytical warehouse tables used for health checks
3 tasksApache Hivesupports defining and testing table schemas and transformations for large datasets during modeling
2 tasksApple macOSruns the desktop environment used to compile notes, run queries and prepare the requirements document
2 tasksApache Kafkastreams and stages change data captures to support incremental migration testing
1 taskApache Subversion SVNmanages and reviews versioned code and documentation for traceable review and defect listing
1 taskApache Cassandrahosts the operational data model and supports middleware patterns for implementing and testing business rules
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The same task, four heights

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ExecuteDo today's task, with fewer mistakesyou are here → ImproveMake it easy for the next person to acceptin the atlas → DecideWork out the right move when it is unclearin the atlas → BecomeLearn the pattern so it stops coming backin the atlas →

Can AI actually do this job?

the honest answer

It can

where it genuinely helps
  • Explain the theory behind the work
  • Draft, tidy and structure your writing
  • Rehearse a hard conversation before you have it
  • Build a study plan that fits your gaps

It cannot

where it stops, completely
  • Be in the room where a data visualization specialist actually works
  • Carry the responsibility when the call is wrong — that weight stays yours
  • Notice what no one wrote down: the hesitation, the thing left unsaid
  • Live with the outcome

What the work pays

two countries, two different measures

United States

this exact occupation · BLS 2025
  • $139,500 a year — the middle: half earn more, half earn less
  • The lowest tenth earn near $86,240; the top tenth near $204,000
  • 67,140 people employed in this occupation

India

the occupation GROUP, not this job · PLFS via ILOSTAT 2025
  • ₹38,298 a month — the median for Professionals, the group this work sits in
  • India publishes pay by broad occupation group, so this covers many jobs besides this one. It is a shape, not a salary.
read this carefullyThese two numbers are not comparable and must not be converted into each other. One is a yearly figure for this job alone; the other is a monthly figure for a whole family of jobs. What travels between them is the pattern, not the amount: experience lifts pay almost everywhere.

Where the evidence lives

open any of it yourself
O*NETThe US Department of Labor's own record of this job7 of these tasks ESCOThe European skills and occupations register9 of these tasks Job descriptionsReal postings, in the wild11 of these tasks US Bureau of Labor StatisticsPay and employment, by occupation2025 figures UK NOS TECDT80851Design data processing systemsnational standard UK NOS TECIS806301Assist in developing data visualisationsnational standard

Close to this work

12 nearby
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Questions people actually ask

You’ll split time between preparing data and making visuals. Mornings often start by checking data warehouse health—looking at Redshift or Hive jobs, ETL failures in Spark, or Kafka pipeline backpressure.

Afternoons go to cleaning and transforming data (SQL in Redshift, Spark scripts), then building charts and dashboards. Expect meetings with data engineers about data marts, metadata, and business rules, and time validating visuals against the source data before release.

Start with Amazon Redshift for columnar analytics and basic SQL, plus Apache Spark for transformations and cleaning at scale. Many teams use Hive or Hadoop for batch storage; knowing how those work helps you understand upstream data.

Also learn how data moves: Kafka for streaming, and tools for metadata and tracking like Atlassian JIRA and Subversion (SVN) for version control. Hands-on with Redshift and Spark gives the fastest path to contributing.

The U.S. Bureau of Labor Statistics (BLS) reports 67,140 employed in this SOC and a median annual wage of $139,500. The lowest tenth earned $86,240 and the top tenth $204,000, according to BLS 2025 data.

Salaries vary by industry, city, and your experience with systems like Redshift, Spark, Kafka, and Cassandra. Specialized skills, like building reliable data marts or maintaining data security, push pay higher.

A Data Visualization Specialist sits between data engineering and BI. You do cleaning, transforming, and validating (like data engineers), but your end goal is visuals and reports that non-technical users can use (like BI developers).

You won’t usually build core Kafka clusters or tune Hadoop nodes daily, but you will implement ETL logic (Spark, Redshift), create data marts, and enforce business rules so visuals are reliable.

Yes, AI can speed up chart templates, suggest SQL or Spark code, and summarize datasets, but never trust it alone. Always validate AI-generated queries against the source data and test results in your ETL pipeline.

Don’t feed sensitive data into public AI services. Keep data security rules: use internal models or anonymized samples, log AI suggestions in your JIRA or version control (SVN), and have a human review before publishing dashboards.

Build a small end-to-end project: load a CSV into Redshift or DynamoDB, transform it with Spark, store a cleaned table or data mart, then make a dashboard. That covers import, clean, transform, validate, and visualize tasks.

Learn SQL well, practice Spark for ETL, and get comfortable with a visualization tool (your employer may use Tableau, Power BI, or a web stack). Document everything in a repo and track tasks in JIRA as if you were on a team.

Data validation and understanding data quality. Knowing how to inspect, validate, and reconcile data across Redshift, Hive, or Cassandra prevents bad dashboards from reaching users.

You’ll use SQL, Spark, and metadata frameworks to find mismatches, then write tests or stored procedures to enforce business rules. Employers notice when you reduce incidents by catching errors before a dashboard is published.