Project: Basic Setup of End-to-End Analytics (Analytics Engineer / Data Analyst)

Budget: 27000 UAH Deadline: 28 days

For the complete Iteration 1, I wouldn't focus on 9000 UAH - this is more of a budget for a short technical analysis or scheme, rather than for DWH, ELT, dbt models, attribution, and BI. A realistic MVP estimate is from 100,000 UAH, with a timeline of 3-4 weeks after access and verification of advertising sources.

I propose the following stack:
> BigQuery as DWH - fits well with GA4, has a cheap start and normal speed for marketing dashboards
> Airbyte Cloud or self-hosted Airbyte for PostgreSQL, Stripe, GA4, and advertising accounts - if the connectors for advertising networks do not cover everything, we will gather part of the cost data through API or export
> dbt for models - so that Last Non-Direct Click, Day 0, currencies, and Creative-level expenses are not just a set of queries, but a reproducible model
> Looker Studio for the first dashboard, Metabase if internal filters and access are needed

Monthly infrastructure at the start - approximately $50-250 for BigQuery, Airbyte or Fivetran, and BI, but Fivetran or Funnel.io can raise the bill to $300-800, depending on the number of accounts and sources. We can keep it simple - start with BigQuery, Airbyte, dbt, and Looker Studio, and only connect expensive services if they really save time.

I suggest moving through a short data schema design, then connecting raw sources, then dbt dashboards, and then verifying metrics on control transactions. Separately, we will provide the backend with instructions on which UTM, click_id, client_id, user_id, and Stripe metadata to write, at what moment, and how not to break attribution after payment.

From you, we need access to PostgreSQL read-only or a dump, Stripe, GA4, advertising accounts, examples of 5-10 real payments, and the logic of what exactly is considered Lead and Sale Day 0.

Clarifications without which the estimate should be kept as preliminary:
> which advertising networks are critical in the first iteration - Meta, TikTok, affiliate, CPC networks
> is user_id already consistently passing from registration to Stripe metadata, or does this still need to be worked out with the backend

Similar cases from Ingello:
> https://business.ingello.com/vorfahr - SaaS, Stripe, automation of product processes and data
> https://business.ingello.com/prime-eva - complex accounting and integration processes for business
> https://systems-fl.ingello.com/ua - Ingello Systems profile for FLH =) Expand

Budget: 9000 UAH Deadline: 8 days

Hello, Denis! I am Nina — the manager of data engineer Valentin. The technical specification is prepared at the level of a top architect. Valentin understands the difference between "just exporting data" and building a Single Source of Truth with a Last Non-Direct Click attribution model.

Proposed stack:

DWH: Google BigQuery (ideal for marketing, natively consumes raw logs from GA4, very cheap on-demand pricing).

ELT: Airbyte (self-hosted on your VPS). No Fivetran/Airbyte Cloud, so you won't pay for row volume. We will use ready-made connectors for Postgres, Stripe, and GA4, and we will connect advertising accounts through Python scripts.

Transformation: dbt Core (models will be incremental, versioned, and fully covered by data tests).

BI: Looker Studio (for the first iteration of the Day 0 dashboard, its features will be more than enough, and it is free).

Infrastructure cost estimate: Up to $20–$30 per month (the cost of a basic VPS for Airbyte + minimal costs for queries in BigQuery at your volumes). Looker Studio and dbt Core — $0.
MVP timeline: 6–8 days (thanks to AI automation, Valentin will build pipelines and dbt models three times faster than the market).

Regarding the budget: The specified budget of 9,000 UAH will objectively cover only the audit, data schema design, and technical specification for the backend developer. Full implementation of the entire Iteration 1 (integration of Postgres + Stripe + Advertising + dashboard with drill-down to creative) turnkey — 32,000 UAH (we are ready to split it into 2 sprints).

Please write to us in private messages, Valentin is ready to share the scheme linking user_id -> click_id -> Stripe metadata for your backend team. Expand

Budget: 9000 UAH Deadline: 11 days

Hello! I understood the task: to consolidate PostgreSQL, Stripe, GA4, and advertising accounts into one repository and build Day 0 analytics with drill-down from source to creative, with Last Non-Direct Click attribution.

For the stack for the first iteration, I suggest the following. BigQuery as the repository, since you already have a Google ecosystem through GA4, a cheap start on an on-demand tariff, and native integration with Looker Studio. Data collection on Airbyte, self-hosted on a small VPS, to avoid paying for row volume like in Fivetran. Connectors to PostgreSQL, Stripe, and GA4 are available out of the box; for the uncovered advertising accounts, we will create our own connector in Python. Modeling on dbt, so that transformations are versioned, covered by tests, and views are built incrementally. BI on Looker Studio to start, free and sufficient for the Day 0 dashboard, later if needed, Metabase.

Separately, I will immediately prepare a clear instruction for your backend so that tracking does not break: what specific tags to write, click_id, UTM, client_id, and where to place them in the metadata of Stripe Charge or Customer, so that the connection of user_id plus transaction plus marketing tag is maintained throughout. I will consolidate expenses into a hierarchy of source, campaign, adset, creative, and will convert daily into a single currency.

Regarding the infrastructure for a month for the MVP, it turns out to be not much. BigQuery at your volumes is a few dollars on-demand, Airbyte self-hosted is just the price of the VPS, Looker Studio is free, meaning the starting infrastructure stays within a few tens of dollars a month. I will calculate more accurately when I see the data volumes and the list of sources.

I have practical experience with BigQuery, dbt, and writing custom connectors in Python, and I understand the data structure of Stripe. I am ready to start with the design of DWH and ELT for Iteration 1. Please let me know how many advertising accounts there are now and if there is already any tracking layer on the backend, so I can more accurately assess the volume for the connectors?

Budget: 10000 UAH Deadline: 12 days

Hello. I am ready to take on your MVP, I have relevant experience in building marketing analytics from scratch.

We will use Google BigQuery for storage, and for the tool, we will take Airbyte, which will cover all your sources from PostgreSQL to advertising accounts. For transformations, we will use dbt core to ensure the models are reliable, and for visualization, Looker Studio.

In terms of pricing, BigQuery will cost about $10-20 per month, Airbyte Cloud $100 depending on the volume of rows, dbt and Looker Studio are free, so in total, you can expect around $120. I am ready to collaborate.

Feel free to write.)

Budget: 10000 UAH Deadline: 10 days

Hello!

I have experience in building data/automation solutions for marketing analytics, API integrations, PostgreSQL, Stripe, Google Sheets/BI, n8n/Python pipelines, and SQL modeling. Your task is clear to me: it is necessary to consolidate disparate sources into a single system where the exact Day 0 return on investment can be seen by Source → Campaign → Adset/Term → Creative.

For Iteration 1, I would suggest the following stack:

**DWH:** Google BigQuery
An optimal choice for MVP, as it is well-suited for marketing analytics, easily scalable, has native integration with GA4, conveniently connects to Looker Studio/Metabase, and allows for quick SQL showcase building without complex infrastructure.

**ELT:** Airbyte or Fivetran
For MVP, I would start with Airbyte if flexibility and cost control are important. If a fully managed approach without infrastructure support is needed, Fivetran can be considered.
Sources for import: PostgreSQL, Stripe, GA4, Meta Ads, TikTok Ads, affiliate/ad network cost data. If there are no ready-made connectors for specific advertising or affiliate sources, a custom Python connector can be written via API.

**Transformations / Data Modeling:** dbt + SQL
I would build a layer of raw data, staging models, and final marts:

* `stg_users`
* `stg_stripe_payments`
* `stg_ga4_events`
* `stg_ad_costs`
* `dim_campaigns`
* `dim_creatives`
* `fact_leads`
* `fact_sales`
* `mart_day0_performance`

The main logic: normalize all expenses into a single structure Source → Campaign → Adset/Term → Creative, link users with UTM/click_id/client_id, Stripe transactions, and the first/last marketing session. For MVP, we can start with the Last Non-Direct Click model and then expand to other attribution models.

**BI:** Looker Studio or Metabase
For a quick MVP, I would suggest Looker Studio if the team is already working in the Google ecosystem. If more flexible work with SQL, filters, roles, and internal dashboards is needed, Metabase is also a good option.

In the "Day 0 Performance" dashboard, I would create a drill-down:
Source → Campaign → Adset/Term → Creative

Key metrics:
Spend, Clicks, Leads, CPL, Sales Day 0, CPS, Revenue Day 0, ROAS Day 0.

I will separately prepare instructions for the backend team:

* which UTM parameters need to be stored;
* how to store click_id: `fbclid`, `ttclid`, `gclid`, affiliate click id;
* how to store GA client_id/session_id;
* at what point to record attribution data in PostgreSQL;
* which fields to pass in Stripe Metadata: `user_id`, `source`, `campaign`, `adset`, `creative`, `click_id`, `client_ Expand