Hire an AI spatial data engineer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A source-by-source check of format, coordinate system, encoding and field quality
• A target schema with field names, types and allowed values
• Pipeline steps from read to write, with a check after each step
• A refresh and monitoring plan, if your data updates
• Draft scripts for the steps you ask for, marked as not run
• A list of known issues, assumptions and the checks left for you

How it does the job well

• Never assume a source's coordinate system is right. If it is missing or doubtful, write VERIFY and say how to confirm it, for example from the data owner or a known landmark.
• Never modify the original files. The pipeline reads from the source and writes to a new location.
• Plan a check after every transformation: geometry validity and field completeness, plus row counts in and out.
• Plan a log for every step with settings and output row counts.
• Plan the run to be repeatable (same result on a second run), driven by a config file for paths, coordinate system codes and field maps, and to stop early with a clear message on missing or malformed input.
• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.

Mistakes it avoids

• Fixing data by hand when a script would do it again next month.
• Reprojecting without confirming the source coordinate system.
• Hard-coded paths or codes in scripts.
• Silent fixes. Every change is logged.
• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.

What a good result looks like

An example, not your task: You have shapefiles, CAD files, spreadsheets with coordinates and a government download, and need one clean dataset.

• Sources and assumptions. Each source with its format, coordinate system, encoding and known problems, and how each was confirmed.
• Target schema. The fields, types and allowed values every source must map to.
• Pipeline steps. Each step from read to write, with the tool and the check that follows it.
• Quality checks. Geometry and field checks, what a pass looks like and what happens on a fail.
• Refresh and monitoring. How and when the pipeline re-runs, what it ignores and what raises an alert.
• Draft scripts. Scripts for the steps you asked for, marked as not run.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Kent Beck would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Assess each source. It notes format, coordinate system, encoding, fields and quality for each file. A missing or doubtful coordinate system is flagged, never assumed.
• Set the target schema. It sets field names, types, allowed values and how empty values are written, so every source maps to one standard.
• Plan the pipeline. It plans read, clean, convert, check and write. Originals are never changed, and each step records its settings and row counts.
• Plan the checks and failure rules. It adds geometry and field checks after each step. The run stops with a clear message if an input is missing or malformed.
• Plan the refresh. It plans settings kept in a config file, re-runs that give the same result, change detection and a check that the run finished and the volume looks right.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id gis-spatial-data-engineer only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/gis-spatial-data-engineer/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Your data sources and their formats, such as shapefile, CSV with coordinates, KML or a file geodatabase
• Where the clean data must go and in what format
• Field names and types you want in the result, if you have a standard (optional)
• How often the data updates, and from where
• The tools you can use, such as Python, GDAL, FME or ArcGIS (optional)

Helpful context (optional)

• The metadata or readme that came with each download.
• Ten to twenty sample rows from each source, so field types can be judged.

Tools and access

• Claude, ChatGPT or Gemini with file upload: It can read file metadata and sample rows and spot coordinate and field problems in them.

Functions and deliverables

• Spatial data engineer agent: Plan a pipeline that turns messy map data into clean, standard files, with coordinate fixes, field rules, quality checks and a repeatable run.
  • A source-by-source check of format, coordinate system, encoding and field quality
  • A target schema with field names, types and allowed values
  • Pipeline steps from read to write, with a check after each step
  • A refresh and monitoring plan, if your data updates
  • Draft scripts for the steps you ask for, marked as not run
  • A list of known issues, assumptions and the checks left for you
  Source: Michael Sitarzewski and Agency Agents contributors.
• Data extraction pipeline builder: Turn a website, file, or API source and field list into extraction code with validation checks.
  • Extraction script
  • Clean CSV or JSON file
  • Validation result
  • Cost and quota estimate
  Source: Alireza Rezvani.
• Data pipeline builder: Describe where your data starts and who uses it. Get a data pipeline design, load code, quality checks and a run schedule.
  • A written pipeline design with the reasons for each choice
  • Extract and load code for each source
  • A reporting data model, such as a star schema
  • Data quality checks for gaps, repeats and late records
  • A schedule and fixes for common faults
  Source: Alireza Rezvani.
• Agent database setup planner: Plan the database your AI agents need. Get a schema map, migration steps, access rules and a staging test plan that keeps existing data safe.
  • A schema map: the tables, columns, keys and indexes for jobs and activity
  • Migration files in order, forward only, with what each one changes
  • An access rule for each table, and which key belongs where
  • A staging test plan using made-up rows
  • A rollback and recovery plan
  • A list of missing inputs and checks that did not run
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.
• Kent Beck: Plan a software change using Kent Beck’s recorded approach. List the cases, take small steps and keep code tidying separate from new behaviour.
  • Your problem worked on the way Kent Beck would: their questions, their method, their push-back
  • The answer or the work, in Kent Beck's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You have shapefiles, CAD files, spreadsheets with coordinates and a government download, and need one clean dataset.
• The coordinate system is missing, mixed or wrong, and you need a safe way to fix it.
• You do the same conversion every month and want it as a repeatable script, not hand work.
• You need to publish data to PostGIS, a GeoPackage or ArcGIS Online and want the checks planned first.

Limits to check before starting

• You need one map made once. Use the GIS analyst.
• You need statistics on the data after it is clean. Use the Spatial data scientist.
• You need a live web map or API for the data. Use the Web GIS Developer agent.
• You need to read objects out of images with a model. Use the Geospatial machine learning planner.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack and add it to your AI as a skill. If you cannot, paste SKILL.md and then references/source-agent.md into a new chat.
• List each source with its format and where you got it. Paste a few sample rows and any metadata files.
• Say where the clean data must end up. Type "Plan the pipeline and flag every coordinate or field problem." Your AI asks once for anything missing.
• Read the source check first. Confirm the coordinate system of each file with its owner before you trust it.
• Run the draft scripts on a copy of your data. Your AI cannot run them for you.
• Compare row counts and sample records before and after each step. Keep the log with the data.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/gis-spatial-data-engineer for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.