If the team already spends money on acquisition, but reports are assembled manually, events do not reconcile, attribution is unclear, or research lives in spreadsheets, I build the first operational layer in 2-4 weeks: reporting, event QA, alerts, LLM-assisted research, a data layer, or an internal interface.
My scope is data, events, integrations, reporting, research, and operational tools around growth and product workflows. Campaign management and media buying remain with your team or agency.
The output is clearer data, alerts, reporting, and an operational tool for the team.
When a team already spends money on acquisition, the bottleneck is often not campaign setup but the data around it. These are six common situations where a focused engineering phase can help.
Ad platforms, MMPs, product analytics, and internal reports show different results, and the team cannot tell which source to trust.
The team spends hours exporting data, updating spreadsheets, taking screenshots, and preparing recurring summaries for marketing, product, or management.
Events, postbacks, attribution windows, sources, UTM parameters, SKAN/MMP data, and discrepancies between systems need to be checked.
The team needs explicit rules, alerts, and sanity checks to notice anomalies, suspicious sources, missing data, and abrupt changes.
Partners, competitors, segments, creatives, product hypotheses, or market signals are found manually and updated irregularly.
The tools exist, but the team still needs a custom layer for APIs, exports, rules, reporting, alerts, and an operational interface.
One sprint, one operational result around advertising and product data: a report, event QA, alerts, a research tool, a data layer, or an internal interface.
One recurring report across ad platforms, MMP data, product events, spreadsheets, and internal sources, without rebuilding it manually every week.
Review events, postbacks, traffic sources, UTM parameters, attribution windows, MMP/SKAN data, and discrepancies between systems.
Rules and notifications that surface unusual sources, sharp deviations, data gaps, and cases that need a person to investigate.
A tool that collects sources, filters them with explicit rules and an LLM, produces shortlists, and sends recurring reports for human review.
A focused backend layer for APIs, exports, spreadsheets, events, normalization, and delivery into reports or an operational interface.
A working interface for reviewing campaigns, events, statuses, hypotheses, reports, tasks, or research shortlists.
We usually start with a focused assessment, then move to a sprint or a larger phase. Support is for tools already in use, not an undefined development subscription.
Review events, data sources, reporting, manual work, and risk points. The output is a map of the problem, a sensible first sprint result, and a recommended next step.
If a Working Tool Sprint starts within 14 days, the assessment fee can be credited toward the sprint.
Build the first operational tool: a report, data layer, monitoring workflow, research tool, event QA, or internal interface.
For work that does not fit one sprint: several data sources, teams, platforms, event flows, integrations, or consecutive delivery phases.
For tools already in use: monitoring, incident review, small improvements, advice, and development without losing context. New sources, major features, integrations, and logic changes are estimated separately.
No company names, confidential details, or internal data. These are types of problems that reflect prior experience and can be rebuilt from scratch around your data and rules.
Data arrives from multiple platforms, event flows, spreadsheets, and internal reports. The team manually searches for discrepancies and weak points.
A consolidated report, validation rules, alerts, and a queue of cases that require attention.
The team sees discrepancies, anomalies, and investigation tasks sooner.
The team manually searches for partners, competitors, segments, product hypotheses, or market signals.
Source collection, rule-based filtering, LLM analysis, a shortlist, and a recurring report.
Less manual research, a regularly updated base, and explicit selection criteria.
Events, traffic sources, and reports do not form one reliable picture. It is unclear where data is lost and what should be checked first.
An event map, postback checks, exports, discrepancy analysis, a QA report, and priorities for the first fixes.
The team understands which data can be used, where the risks are, and what to fix first.
This track is about data and operational tools around acquisition, not about running paid media. The boundary is explicit so expectations remain aligned.
I work with data, events, reporting, integrations, and tools around acquisition. Campaign setup and media buying remain with your team or agency.
I do not promise sales growth, ROAS improvement, or lower CAC. I am accountable for the agreed technical result.
I work only with lawful clients, products, and transparent methods. I do not support gambling, gray financial schemes, platform-rule circumvention, traffic cloaking, or user deception.
The first sprint can include data-quality rules, alerts, QA reports, and queues of cases for human validation.
I do not reuse code, datasets, configurations, or internal materials from previous employers or clients.
The work is based on public sources, your own data, and systems for which you have legitimate access rights.
We do not begin with a large build. First we understand the data problem, define the first useful result, and only then implement the tool.
Which data sources exist, where discrepancies appear, what is done manually, and which result the team needs.
We determine whether the task fits this track and whether the next step should be an assessment or a sprint.
I review events, sources, attribution, reporting, access, limitations, and the expected operational outcome.
We agree what is included, what is not, the timeline, budget, and acceptance criterion.
I build the tool, show working parts, clarify details as needed, and keep the project within the agreed scope.
Demonstration, alert setup, instructions, stabilization, and a plan for the next phase.
I work across engineering, product, data, and delivery. I turn poorly defined data problems into a focused working result: backend, APIs, LLM workflows, event/data pipelines, reporting, alerts, or an internal interface.
I personally lead the first conversation, data assessment, first-release scope, technical design, implementation, and quality control. Communication is direct, and I take on a limited number of projects at a time.
Short answers about advertising, assessments, agencies, MMP integrations, and data boundaries.
No. I am not a media buyer. I work with data, events, reporting, integrations, research, and internal tools around advertising and product workflows.
Yes. It is often the right entry point when the team first needs to identify where the data problem actually is and which first operational tool is worth building.
The introductory call qualifies the task and selects the next step. It does not include solution design or a detailed specification.
The paid Growth/Data assessment reviews sources, discrepancies, manual work, risks, the first operational outcome, and the next step. If a Working Tool Sprint starts within 14 days, the assessment fee can be credited toward it.
Yes, when the clients, products, and acquisition methods are lawful and transparent, and the work does not involve bypassing platform rules or misleading users.
I can work on reporting, events, integrations, data-quality checks, and internal tools, including in a white-label format.
Yes, when access and the task are clear. I can review events, postbacks, exports, and discrepancies, and build a focused reporting or QA layer.
Yes. A first sprint can include validation rules, alerts, reports, and queues of cases that need the team's attention. It does not replace growth or product expertise, but it helps surface discrepancies and suspicious changes sooner.
I do not transfer code, configurations, datasets, or internal materials from previous employers or clients. New tools are built from scratch using your data and lawful public sources.
Describe the available data sources, where discrepancies appear, what is currently manual, and which operational result the team needs.
I will review the task and usually reply within one business day. If it requires a closer review, I will get back with a short assessment within 1-2 business days.