Christian Callahan / Independent practice

Open to roles & project work

Data
engineer.

I design and build SQL and Python pipelines, data models, and reporting systems. I make the logic explicit, reconcile the results, and document the handoff.

See my work Let’s talk

Inside NY SPARCS

NY SPARCS starts with public, de-identified hospital discharge files. Field names change between annual releases.

For organizations big and small.
Across industries.

SQL / Python / Data pipelines

Selected work

Projects

I built these projects with public data.
Try the apps or see how they work.

Healthcare data engineering

NY SPARCS

From hospital CSVs to a reporting app.

I used Python and DuckDB to normalize New York’s annual discharge files, build reporting tables, and feed a Streamlit app.

Pipeline map

SourceAnnual discharge CSVs
TransformPython + DuckDB SQL
StoreParquet reporting marts
ExploreStreamlit dashboard

View repository Read the code

NY SPARCS executive dashboard, showing 2024 discharge and cost summaries from the project repository — Project screenshot · NY hospital discharge data

How I built it

Source: Public, de-identified discharge CSVs from New York. Field names and coding systems change between annual releases.
Data flow: CSV → header normalization → DuckDB staging → SQL transformations → Parquet marts → Streamlit.
Checks: Test fixtures check header cleanup and reconcile the reporting tables with the source. Small groups are suppressed in published comparisons.
Decisions: I used DuckDB and Parquet to keep the build local and easy to inspect. The downloader records source details and limits retries.
Limits: The data is annual, so it won’t answer real-time questions. Charges aren’t revenue. Small demo tables are included; this portfolio doesn’t run the full pipeline.

SQL models Mart tests

Operational data modeling

ED Operations

Checking emergency-department data with SQL and Python.

I modeled Scotland’s emergency-department data by site and month, then built SQL and Python versions so I could compare the results.

Pipeline map

SourceMonthly site CSVs
ModelPython + DuckDB versions
CompareReconciliation tests
ExploreStreamlit reporting

View repository See the checks

ED Operations Streamlit forecast dashboard, including a comparison whose uncertainty interval includes no improvement — Project screenshot · forecast improvement is uncertain

How I built it

Source: Monthly CSVs from Public Health Scotland. Each row covers one site and month, rather than an individual patient visit.
Data flow: The raw files feed separate Python and DuckDB models. I compare their results before using them for reporting.
Checks: Tests check keys and counts, and compare rates within a defined tolerance. Combined rates are calculated from counts, rather than by averaging percentages.
Output: A Streamlit app and saved dashboard outputs, with documented site, health board, and calendar tables.
Limits: The forecast’s uncertainty interval includes zero improvement, so I can’t call it a clear win. I built this independently of NHS Scotland and Public Health Scotland; it doesn’t show a measured change in hospital operations.

Fact model Data model

More projects

Other builds

Customer analytics

Ecommerce Retention & Growth

I built subscription features using data available before each prediction cutoff. Includes a real-data sample and a separate synthetic demo.

View repository

Financial modeling

AutoModeler

Financial data to Excel models with linked formulas. Stops when a balance-sheet adjustment exceeds the configured limit.

View repository

Text classification

Ticket Intel

TF-IDF and Naive Bayes for ticket classification. The app uses synthetic examples; code and evaluation are on GitHub.

View repository

Model evaluation

SignalForge

I compared churn models on IBM Telco data and documented how I evaluated them.

Read the case study

My approach

How I work

I build pipelines I can explain: where the data came from, how it changed, and how I checked the result.

Source
Understand.
I start with where it comes from, what each row means, what’s missing, and how often it changes.
SPARCS: annual schema changes
Model
Build.
I define the output before building the pipeline. Then I keep the extraction, staging, and transformation steps easy to follow.
ED Operations: the site-month model
Checks
Check.
I compare keys, counts, and totals against the source. If something doesn’t line up, the check should fail where I can see it.
ED Operations: SQL reconciliation tests
Hand-off
Document.
I document the sources, field definitions, refresh steps, and limits so someone else can pick it up without guessing.
SPARCS: source, build, and demo instructions

Background

Experience

My background is in healthcare reporting and business intelligence. That’s where I’ve worked on turning operational data into reports people can use.

Independent practice

CGC Labs

Founder / Data engineering

I started CGC Labs for my independent data engineering work. I focus on getting data out of legacy systems, cleaning it up, checking it, and documenting how it works.

Professional experience / McCook, Nebraska

Community Hospital

Business Intelligence Analyst

I built Tableau dashboards and automated recurring reports. I also created SQL staging for an electronic medical record migration and validation checks for billing data.

I keep this work high-level here. Employer code, internal systems, and patient data stay private.

Beyond this portfolio

Elsewhere.

Independent practice

CGC Labs

My independent data engineering work and consulting.

cgclabs.org

Product in progress

MeasureMap

I’m building a registry for metric definitions, owners, formulas, and source references.

measuremap.cgclabs.org

Contact

Let’s talk.

Hiring a data engineer or need help with a project?
Tell me what you’re working on.

Email mechristian.g.callahan@gmail.com Find me on GitHub

Christian Callahan / Independent practice

Open to roles & project work

Data
engineer.

I design and build SQL and Python pipelines, data models, and reporting systems. I make the logic explicit, reconcile the results, and document the handoff.

See my work Let’s talk

Inside NY SPARCS

NY SPARCS starts with public, de-identified hospital discharge files. Field names change between annual releases.

For organizations big and small.
Across industries.

SQL / Python / Data pipelines

Selected work

Projects

I built these projects with public data.
Try the apps or see how they work.

Healthcare data engineering

NY SPARCS

From hospital CSVs to a reporting app.

I used Python and DuckDB to normalize New York’s annual discharge files, build reporting tables, and feed a Streamlit app.

Pipeline map

SourceAnnual discharge CSVs
TransformPython + DuckDB SQL
StoreParquet reporting marts
ExploreStreamlit dashboard

View repository Read the code

How I built it

Source: Public, de-identified discharge CSVs from New York. Field names and coding systems change between annual releases.
Data flow: CSV → header normalization → DuckDB staging → SQL transformations → Parquet marts → Streamlit.
Checks: Test fixtures check header cleanup and reconcile the reporting tables with the source. Small groups are suppressed in published comparisons.
Decisions: I used DuckDB and Parquet to keep the build local and easy to inspect. The downloader records source details and limits retries.
Limits: The data is annual, so it won’t answer real-time questions. Charges aren’t revenue. Small demo tables are included; this portfolio doesn’t run the full pipeline.

SQL models Mart tests

Operational data modeling

ED Operations

Checking emergency-department data with SQL and Python.

I modeled Scotland’s emergency-department data by site and month, then built SQL and Python versions so I could compare the results.

Pipeline map

SourceMonthly site CSVs
ModelPython + DuckDB versions
CompareReconciliation tests
ExploreStreamlit reporting

View repository See the checks

How I built it

Source: Monthly CSVs from Public Health Scotland. Each row covers one site and month, rather than an individual patient visit.
Data flow: The raw files feed separate Python and DuckDB models. I compare their results before using them for reporting.
Checks: Tests check keys and counts, and compare rates within a defined tolerance. Combined rates are calculated from counts, rather than by averaging percentages.
Output: A Streamlit app and saved dashboard outputs, with documented site, health board, and calendar tables.
Limits: The forecast’s uncertainty interval includes zero improvement, so I can’t call it a clear win. I built this independently of NHS Scotland and Public Health Scotland; it doesn’t show a measured change in hospital operations.

Fact model Data model

More projects

Other builds

Customer analytics

Ecommerce Retention & Growth

I built subscription features using data available before each prediction cutoff. Includes a real-data sample and a separate synthetic demo.

View repository

Financial modeling

AutoModeler

Financial data to Excel models with linked formulas. Stops when a balance-sheet adjustment exceeds the configured limit.

View repository

Text classification

Ticket Intel

TF-IDF and Naive Bayes for ticket classification. The app uses synthetic examples; code and evaluation are on GitHub.

View repository

Model evaluation

SignalForge

I compared churn models on IBM Telco data and documented how I evaluated them.

Read the case study

My approach

How I work

I build pipelines I can explain: where the data came from, how it changed, and how I checked the result.

Source
Understand.
I start with where it comes from, what each row means, what’s missing, and how often it changes.
SPARCS: annual schema changes
Model
Build.
I define the output before building the pipeline. Then I keep the extraction, staging, and transformation steps easy to follow.
ED Operations: the site-month model
Checks
Check.
I compare keys, counts, and totals against the source. If something doesn’t line up, the check should fail where I can see it.
ED Operations: SQL reconciliation tests
Hand-off
Document.
I document the sources, field definitions, refresh steps, and limits so someone else can pick it up without guessing.
SPARCS: source, build, and demo instructions

Background

Experience

My background is in healthcare reporting and business intelligence. That’s where I’ve worked on turning operational data into reports people can use.

Independent practice

CGC Labs

Founder / Data engineering

I started CGC Labs for my independent data engineering work. I focus on getting data out of legacy systems, cleaning it up, checking it, and documenting how it works.

Professional experience / McCook, Nebraska

Community Hospital

Business Intelligence Analyst

I built Tableau dashboards and automated recurring reports. I also created SQL staging for an electronic medical record migration and validation checks for billing data.

I keep this work high-level here. Employer code, internal systems, and patient data stay private.

Beyond this portfolio

Elsewhere.

Independent practice

CGC Labs

My independent data engineering work and consulting.

cgclabs.org

Product in progress

MeasureMap

I’m building a registry for metric definitions, owners, formulas, and source references.

measuremap.cgclabs.org

Contact

Let’s talk.

Hiring a data engineer or need help with a project?
Tell me what you’re working on.

Email mechristian.g.callahan@gmail.com Find me on GitHub

Dataengineer.

Projects

Other builds

Ecommerce Retention & Growth

AutoModeler

Ticket Intel

SignalForge

Understand.

Build.

Check.

Document.

Education

CGC Labs

MeasureMap

Let’s talk.

Dataengineer.

Projects

Other builds

Ecommerce Retention & Growth

AutoModeler

Ticket Intel

SignalForge

Understand.

Build.

Check.

Document.

Education

CGC Labs

MeasureMap

Let’s talk.

Data
engineer.

Data
engineer.