Open to Principal and Staff data engineering roles.
Jonathan Hazeley
Principal-Level Data Engineer
Migrations fail on trust, not on code. I lead Databricks migrations and build the proof that lets a business commit to the platform.
Charlotte, North Carolina
- rows migrated and proven on one pilot
- 678M
- automated checks per validation run
- 114
- business metrics standardized in one glossary
- 100+
- EBITA increase the pipelines contributed to
- 50%
rows migrated and proven on one pilot
automated checks per validation run
business metrics standardized in one glossary
EBITA increase the pipelines contributed to
The evidence
Selected work
A few problems worth explaining in full.
Proving a Migration, Not Just Doing One
Ten source tables moving platforms. No way to prove any of them arrived intact.
- rows migrated on the pilot
- 678M
- structural match on every validation run
- 100%
- automated checks per run
- 114
- average difference against source (2% tolerance)
- 0.01%
rows migrated on the pilot
structural match on every validation run
automated checks per run
average difference against source (2% tolerance)
- Databricks
- Lakeflow Declarative Pipelines
- Lakehouse Federation
- Snowflake
- Delta Lake (MERGE, Liquid Clustering)
- Medallion architecture
The Databricks Catalog Other Engineers Build Inside
Every engagement re-derived the same decisions. The knowledge lived in whoever was staffed.
- Databricks engineering skills codified
- ~90
- distribution surfaces from one source
- 4
Databricks engineering skills codified
distribution surfaces from one source
- Databricks
- Python
- FastMCP
- Google Cloud Run
Statewide Transportation Data, Made Trustworthy
A feed lapsed. The dashboard still rendered, with a gap nobody could see.
- improvement in statewide reporting availability
- 30%
- faster onboarding for a new data source
- 60%
improvement in statewide reporting availability
faster onboarding for a new data source
- Databricks
- Delta Live Tables
- PySpark
- Azure OpenAI
Hospital Billing, Modeled Once
Every new question became another query against raw Epic tables. No two reports agreed.
- fact and dimension tables refactored off Epic Clarity and Caboodle
- 100+
- delivery owned, Epic source to Power BI model
- End-to-end
fact and dimension tables refactored off Epic Clarity and Caboodle
delivery owned, Epic source to Power BI model
- Databricks
- SQL
- Power BI
- Epic Clarity / Caboodle
The Metrics Behind a 50% EBITA Increase
Everyone had the data. Nobody had a reason to believe it.
- EBITA increase (~$50M) the pipelines contributed to
- 50%
- Databricks pipelines integrating IoT and SaaS sources
- 15+
EBITA increase (~$50M) the pipelines contributed to
Databricks pipelines integrating IoT and SaaS sources
- Databricks
- SQL
- Python
- Power BI
Where I am
Currently
Lead Data Engineer Consultant at Lovelytics. Databricks’ four-year Partner of the Year, backed by Databricks Ventures. I am the client-facing technical lead on two to three concurrent lakehouse builds and migrations, and I set the Databricks delivery standard the engagement teams work inside.
Full experienceVerified
Credentials
Certified on the platforms I build on. Every one links to its issuer.
- Databricks Certified Data Engineer Associate
- AWS Certified Cloud Practitioner
Plus 13 training and course credentials across Data Engineering, AI, Cloud & Delivery.
The toolkit
What I work with
- Languages & processing
- SQL
- Python
- PySpark
- Apache Spark (Structured & Streaming)
- Working knowledge:
- Pandas
- NumPy
- Databricks & lakehouse architecture
- Databricks
- Unity Catalog governance
- Lakeflow Declarative Pipelines
- Delta Live Tables
- Delta Lake
- Databricks Asset Bundles
- Auto Loader
- Medallion architecture (bronze/silver/gold)
- Data Vault 2.0
- Data Mesh
- Dimensional modeling (SCD Type 1/2)
- Migration architecture
- Storage & query engines
- Snowflake
- BigQuery
- Postgres
- Working knowledge:
- Amazon Athena
- Presto
- Trino
- Cloud & delivery
- Azure (ADLS, Fabric)
- AWS
- GCP
- ETL / ELT
- CI/CD for data
- Environment promotion
- Azure DevOps
- GitHub Actions
- Git
- dbt
- Fivetran
- Astronomer
- Working knowledge:
- Apache Airflow
- AI & agentic systems
- Azure OpenAI
- Model Context Protocol
- Claude Agent SDK
- Prompt design & engineering
- Agent evaluation
- Analytics & BI
- Power BI
- Epic Clarity / Caboodle
- Heap.io
- Celonis
- Working knowledge:
- Tableau
- Redash
- Practice & leadership
- Databricks migrations
- Data governance frameworks
- Test-driven development for pipelines
- Mentorship & technical guidance
- Multi-engagement delivery
- Level-of-effort estimation
- Discovery workshops
- Stakeholder management
- Agile / Scrum
The person
Outside the work
A principal hire is someone a team lives with for years, not a résumé. This is who that is.
- Boxing
- Salsa, Bachata, Merengue
- Woodworking
Plus 15 countries across 4 regions, most of them where the music I dance to comes from.
Let’s talk
Send the team and the problem it owns. You will get a straight answer on whether I am the right fit, and the two projects closest to it.