Your RevOps team owns the motion.
Definitions, process, routing decisions, stakeholder alignment, and adoption stay with the people closest to the business. I work from those requirements and make the data path underneath them dependable.
Revenue data engineering
Engineering for GTM teams without data engineers.
Your warehouse, CRM, and go-to-market systems should agree on what happened. I build the production pipelines and integrations that connect them—so your RevOps team can work from data they can trust without adding full-time data engineering headcount.
A focused engineering engagement for B2B SaaS teams with revenue data problems that sit between tools, teams, and ownership.
recognition
The symptoms usually show up before the root cause does.
The team is busy, the tools are connected, and yet the answers still need a manual check. That is often a systems problem—not a lack of effort from RevOps.
- CRM records lag behind product or warehouse activity, so sellers are working from yesterday’s context.
- Lifecycle stages, source fields, or campaign IDs are overwritten as data moves between systems.
- Attribution reports disagree because identity, timestamps, and definitions are not carried through the funnel.
- Every new routing or enrichment request becomes another spreadsheet, Zap, or one-off script.
- Someone knows the numbers are wrong, but no one owns the pipeline between the source and the decision.
where this fits
Engineering behind your revenue operation.
This work is designed to make a good RevOps function more reliable. It is not a new operating model, a replacement team, or a promise that every workflow needs custom code.
Engineering owns the connective tissue.
No-code tools can be the right choice for a simple sync. When identity resolution, transformations, cadence, testing, monitoring, or volume outgrow a point solution, I build the small, documented system that your team can operate.
systems we build
The revenue data layer, in practical pieces.
Start with one bottleneck or connect several. Every system is scoped around the definitions, tools, and operating constraints your team already has.
Signal-to-CRM
Move product, marketing, and customer signals into the CRM with identity rules and field mappings that preserve context.
- event and identity mapping
- incremental syncs
- routing-ready fields
Warehouse-activated GTM
Turn modeled warehouse data into actions for sales, marketing, and customer teams without exporting CSVs by hand.
- reverse ETL patterns
- audience and account syncs
- scheduled or event-driven jobs
Attribution foundations
Establish the joins and conventions required to connect acquisition, lifecycle, pipeline, and revenue data.
- source and campaign lineage
- identity and timestamp logic
- documented metric definitions
GTM reliability
Give critical revenue workflows tests, observability, and clear failure paths so issues are found before a forecast meeting.
- data quality checks
- alerts and runbooks
- change history and ownership
Custom integrations
Connect systems that do not have a reliable native path, with an intentionally small surface area and a documented handoff.
- API and webhook integrations
- schema and retry design
- deployment and operations docs
implementation capability
AI-assisted CRM operations, with controls outside the agent.
I design review-first workflows for RevOps and Sales Ops. AI can help surface and explain candidate records; people retain approval authority, and the CRM enforces execution rules.
delivery pattern / account deduplication
Review possible duplicates before a merge.
Preview likely duplicates, run safety checks, and make the decision reviewable before any merge. The assistant can prepare a recommendation, but it cannot approve its own action or override a hard block enforced inside the CRM.
proof of work
A reverse-ETL engine built for the workflow, not the vendor demo.
One documented build moved a CRM workflow from a daily cadence to 15-minute updates, processed roughly 32,000 contact updates per month, and expanded from 1 sync to 15 on the same architecture. The logic was version-controlled, with alerts for failures and a clear operational path.
The initial build took 3 days. The point is not that every pipeline should be custom—it is that the right boundary can make a focused, maintainable build practical.
Read the reverse-ETL build note daily → 15 min CRM update cadence
~32k / month contact updates processed
1 → 15 syncs on one architecture
git + alerts version control and operations
3 days initial build: planning, implementation, and testing
engagement process
A small, inspectable path from problem to operating system.
The first deliverable is clarity about the workflow and its boundaries. Implementation follows only where it improves the path.
Map the path
Trace sources, destinations, identities, definitions, current failure modes, and the decisions the data needs to support.
Choose the first win
Prioritize one workflow with a clear owner, a measurable definition of done, and constraints the build can respect.
Build the path
Implement the pipeline or integration with explicit transformations, incremental logic, tests, and safe retries.
Make it operable
Add monitoring, alerts, runbooks, ownership, and a deployment path your team can inspect and maintain.
Extend deliberately
Use what the first workflow teaches us to decide whether the next sync, model, or attribution layer is worth adding.
capabilities + tools
The stack is a means to a reliable revenue workflow.
questions
Before we write a line of code.
Is this RevOps consulting?
It is engineering for the systems your RevOps team depends on. I can help translate a workflow into technical requirements, but your team remains the owner of process, definitions, and adoption.
Can we start with a project and continue with fractional coverage?
Yes. A focused project can establish the first reliable workflow, documentation, and operating baseline. If ongoing engineering capacity is useful after that, we can continue with fractional coverage; there is no assumption that a project must become a retainer.
When is a custom pipeline worth considering?
Usually when a native integration or no-code path cannot preserve identity and context, meet the needed cadence, express the required transformations, or provide enough visibility into failures. We will test that premise during scoping.
Can you work with the tools we already use?
Yes. Existing warehouses, CRMs, orchestration, and integration tools are the starting point. The goal is a coherent, supportable path—not a forced technology change.
What happens in the first conversation?
We identify the workflow that is creating the most friction, who depends on it, what “correct” means, and what constraints matter. If there is no clear engineering problem yet, I will say so.
Do you replace our RevOps or data team?
No. I provide focused data engineering capacity and leave behind documented work. The people who know your business should continue to own the operating decisions.
Do you guarantee pipeline or revenue outcomes?
No. Revenue outcomes depend on many factors beyond a data system. My accountability is for technical reliability and delivery: a clearly scoped implementation, tested data paths, useful observability, and documentation your team can operate.
next step
Have a revenue workflow your team keeps working around?
Bring the messy version. We can decide together whether it needs a new pipeline, a better definition, or simply a smaller fix.
For agencies and RevOps partners →