Databricks consulting for regulated, audited data.
Lakehouse pipelines designed so a number can be traced back to the file it came from. Built by an engineer working daily on investment data inside an enterprise Azure and Databricks environment, under real governance constraints.
Most Databricks problems are not Spark problems
Environments that need help usually have working code. What they lack is structure. Notebooks multiply, transformation logic gets copy-pasted between them with small undocumented variations, job dependencies live in someone's memory, and a failure at three in the morning produces a stack trace nobody can map back to a business rule.
The result is a platform that is technically capable and practically fragile. Nobody wants to change anything, because nobody is certain what depends on what. Reports are trusted out of habit rather than because their derivation can be shown.
Fixing this is less about Spark tuning than about separating concerns: raw data kept untouched, cleaning kept apart from business logic, rules written down where they can be reviewed, and validation that makes failure loud instead of silent. That work is unglamorous and it is what makes the difference between a lakehouse and a pile of notebooks.
What I build
Medallion architecture design
Bronze, silver, and gold layers with clear responsibilities, so raw data stays reprocessable and every gold figure traces back through documented transformations.
Delta Lake pipelines
Versioned tables with schema enforcement and time travel, so you can answer what the data looked like at the moment a report was produced.
Ingestion frameworks
Configurable intake so onboarding a new source is a configuration change rather than a new notebook, with schema drift handling and arrival alerting.
Data quality and validation
Schema checks, control totals, row count reconciliation between layers, and quarantine paths for records that fail rather than silent drops.
PySpark development and optimisation
Transformation logic written to be read by the next person, plus partitioning and job structure work where runtimes or costs have become a problem.
Migration onto the lakehouse
Moving spreadsheet and scheduled-script processes onto Databricks without losing the undocumented business rules embedded in the process being replaced.
A configurable intake framework beats a notebook per source
The pattern I keep returning to is a single, configurable intake framework rather than bespoke code for each source. New sources are onboarded by declaring their schema, arrival expectations, and validation rules as configuration. The ingestion, quality checking, and landing logic is shared.
The benefit is not elegance, it is maintenance. When ten sources each have their own notebook, a fix to a common bug has to be applied ten times and will be applied to eight. When they share a framework, the fix lands once and the difference between sources is visible as configuration rather than buried in code.
It also changes who can add a source. A declarative configuration can be reviewed and often written by a data analyst who understands the source, rather than requiring an engineer for every addition.
When Databricks is the wrong answer
Databricks is often proposed for problems that do not need it. If you are consolidating a few thousand rows once a month, or automating a single reconciliation, the platform cost and operational overhead are not justified. A Python pipeline, a Power Platform solution, or a well-built reconciliation tool will be cheaper, faster to deliver, and easier for your team to own after handover.
Databricks earns its place when you have genuine data volume, multiple sources requiring governed joins, real lineage and audit requirements, or several people who need to work on the same data without stepping on each other. If your situation does not look like that, I will tell you so at the review stage rather than after you have signed.
Explore further
Common questions
The work is usually one of three things: designing the layer structure and transformation logic for a new lakehouse, stabilising an existing set of notebooks that has grown without structure and now fails unpredictably, or migrating a process off spreadsheets and scheduled scripts onto a platform that can be governed and audited. The engineering is rarely the hard part. Understanding the business rules embedded in the process being replaced usually is.
Three layers. Bronze holds source data as it arrived, unmodified, so it can always be reprocessed and any output traced back to it. Silver holds cleaned, conformed, validated data with types enforced and duplicates resolved. Gold holds the aggregated, business-ready tables that reporting reads. The discipline matters more than the naming: keeping raw data untouched and separating cleaning from business logic is what makes a pipeline debuggable a year later.
Often it is overkill, and I will say so. Databricks earns its cost when you have genuine volume, multiple sources needing governed joins, a requirement for lineage and audit, or a team that needs to collaborate on the same data. For a single monthly reconciliation of a few thousand rows, a well-built Power Platform or Python solution is cheaper, simpler, and easier for your team to own. Choosing the smaller tool is usually the better engineering decision.
Yes. Much of this work is inheriting an environment that grew organically: notebooks with copy-pasted logic, unclear job dependencies, no tests, and intermittent failures nobody has traced. The sequence is instrumentation first so failures become visible, then documenting actual behaviour, then refactoring. Rewriting before understanding is how business rules get silently lost.
Delta Lake gives you versioned tables, time travel, and schema enforcement, which together make it possible to answer what the data looked like when a report was produced. On top of that I build explicit validation: schema checks on ingestion, control totals against source, row count reconciliation between layers, and quarantine paths for failing records. The pipeline should stop rather than publish a figure it cannot verify.
Lakehouse to build, or notebooks to untangle?
A free 30-minute review covers what is actually wrong, what fixing it involves, and whether Databricks is the right tool for your situation at all.