01
Data Pipelines
Batch ingestion, distributed processing, object storage, and orchestration
- Python
- Spark
- Kafka
- AWS S3
- Airflow
ABOUT / 01
RAW TO RELIABLE / ABOUT / ENGINEERING PRACTICE
I build data systems that make complex information usable.
Raw to Reliable is the principle behind my work: preserve source context, validate what changes, and make every output useful at the point of decision.
I work across data engineering, analytics engineering, and business intelligence—building pipelines, models, quality controls, and reporting systems that turn fragmented operational data into reliable analytical outputs.
01
Professional Focus
I’m a New York–based Data Engineer and Analytics Engineer focused on building reliable systems between raw operational data and business decisions.
My work includes ingestion, transformation, orchestration, dimensional modeling, data-quality validation, analytics-ready marts, reporting, and workflow automation. I’m particularly interested in the parts of a system where source complexity, inconsistent definitions, and operational requirements must be converted into structures that people can trust and use.
02
How I Work
- 01
Preserve Context
I retain source metadata and reporting context before simplifying data for analysis.
- 02
Build in Layers
I separate ingestion, staging, core modeling, marts, and presentation so each layer has a clear responsibility.
- 03
Validate Early
I add quality checks before unreliable records become downstream reporting problems.
- 04
Make Work Reproducible
I prefer repeatable pipelines, explicit configuration, idempotent processing, version control, and documented decisions.
- 05
Keep Decisions Reviewable
Scores, transformations, classifications, and automated outputs should remain understandable and open to human review.
- 06
Connect Engineering to Use
A pipeline is not finished when data loads successfully; it is finished when the output supports a clear analytical or operational decision.
03
Systems I Build
02
Analytics Engineering
Staging, core, and mart layers, dimensional models, and governed transformations
- Snowflake
- dbt
- SQL
- Star Schemas
03
Data Quality
Validation rules, automated tests, anomaly checks, and reproducible processing
- Great Expectations
- dbt Tests
- Idempotency
04
Analytics Outputs
Operational reporting, executive reporting, semantic models, and decision-ready datasets
- Power BI
- DAX
- Power Query
05
Product Workflows
Browser integration, local persistence, scoring systems, workflow state, and human review
- Chrome Extension
- SQLite
- Streamlit
06
Technical Communication
Architecture records, debugging notes, implementation decisions, and explicit trade-offs
- Engineering Notes
- Case Studies
- Documentation
04
Current Focus
- 01View SEC Case Study
SEC Financial Data Platform
A developing batch-data system for ingesting, validating, modeling, and monitoring company financial records.
- 02Explore Job Search OS
Job Search OS
A local-first workflow system combining browser ingestion, structured analysis, duplicate protection, persistent tracking, and human review.
- 03Read Engineering Notes
Engineering Notes
Technical records that document system failures, debugging paths, and the engineering decisions behind reliable behavior.
05
Beyond the Pipeline
My background includes business intelligence, enterprise reporting, regulated workflows, and building operational tools for a growing business. That experience shapes how I approach data systems: the technical architecture matters, but so do definitions, exceptions, handoffs, documentation, and the people who depend on the output.
At Solvia One LLC, where I am a Co-Founder and Data Platform Engineer, I work across data infrastructure, product and pricing inputs, operational reporting, API integration, cloud hosting, and workflow automation. The environment requires practical systems that can evolve with the business rather than isolated technical demonstrations.
06
Explore the Work
- 01Explore Work
Work
Data platforms, pipelines, modeling, and analytics systems
- 02Visit the Lab
Lab
Product experiments and local-first workflow systems
- 03Read Engineering Notes
Writing
Debugging records and engineering notes
- 04View Resume
Resume
Experience, projects, skills, education, and certification
- 05Open Contact
Contact
Start a conversation
For roles, technical collaboration, or conversations about data systems, the Contact section on the Homepage includes my direct links.