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Description: We maintain a reporting system for trainings/courses, where each client has a separate Power BI report that retrieves course completion data via the Tovuti LMS API using Power Query (M). One of these reports has started returning a 429 Too Many Requests error, as the query reloads the entire data history each time, and the volume of this history has already exceeded the Tovuti API request limit. Requirements: Rewrite the Power Query so that only new/modified records are loaded during each refresh, using the timestampModifiedAfter parameter in the courseProgresses endpoint (this parameter has been confirmed to exist and work), instead of reloading the entire history. Add retry/backoff logic so that in case of a 429 error, the request pauses and continues working instead of failing with an error. Test the fixes on our actual Tovuti API and confirm that the updates are successful. Briefly document the changes (what and why was changed) so that we can maintain this in the future. Data model context: The report consists of 4 related tables: Teams, Courses, Users, and AssigmentsProgress (fact table pulled from the Tovuti courseProgresses endpoint, linked by teamId, courseId, userId). The AssigmentsProgress table hits the request limit as it grows every time someone starts, takes, or completes a course. Requirements: Confident practical experience with Power Query (M language) and incremental data refresh patterns in Power BI Experience with REST APIs (pagination, date filters, request limits, retry/backoff handling) Ability to read OpenAPI/schema documentation Clear communication in English Outcome: A working, tested Power Query fix integrated into the existing report, with a brief written explanation of the changes. Budget: Fixed price for this task (amount to be discussed based on the proposal)

It is necessary to quickly compile a database of contacts for cosmetologists and cosmetic clinics. Requirements for the result: Format: Excel or Google Sheets. Phone numbers must be in international format, as continuous digits WITHOUT plus signs, spaces, or hyphens (for example: 380**********). Structure: Column A — Clinic name / Name, Column B — Phone number. Volume: at least 200 fresh contacts, without duplicates and empty rows.

Input Data For each product, the following is provided: name; short description; one or several photographs; ready prompt for generating SEO description; ready prompt for generating an image. What Needs to Be Done1. Generating SEO Description insert the name and description of the product into the ready prompt; transfer the result obtained from AI unchanged; formulate: SEO title; SEO description; keywords; product characteristics.2. Filling in Characteristics Fill in the characteristics according to the information obtained from AI. If certain data is insufficient, clarification using AI is allowed without independently inventing characteristics.3. Generating Image Using the ready prompt, generate a new image of the product, using: name; description; existing photo of the product. The image must meet the requirements specified in the prompt. Important The work is performed exclusively according to the ready prompts. All details of the process are shown in the video instructions attached to this brief. It is necessary to carefully follow the instructions and check the result before submission. Result For each product, it is necessary to provide: ready SEO title; SEO description; keywords; filled characteristics; newly generated image. Additional Information We are considering multiple performers for long-term cooperation. Before starting the main work, it is necessary to complete a test task. After a successful test task, the first stage will be processing 150 products. Please indicate the cost of processing 150 products and the completion time. Expected productivity — at least 25-30 products per day. After the successful completion of the first stage, the next batches of products will be transferred. The total volume of work — about 2000 products.

About the project An artistic ballet company from Berlin. We sell tickets to our own productions. Promotion: Meta, Google Search, email (Brevo), out-of-home, partner portals and influencers. What needs to be done Previously our ad budget was spent without proper attribution — we couldn't see where purchases actually came from. We need a clean measurement system: correctly working pixels (Meta / Google / TikTok) and GA4, cross-domain from our landing page to checkout on the ticketing partner's platform, tagging and distinction of all traffic sources, landing-page behavioral analytics (scroll depth, reaching the tickets block), and consolidation of all channels into one readable panel. We're not looking for a hands-on media buyer. Meta ad management is automated through a separate tool, and we run strategy ourselves. What we need from you is to build the data nervous system that decisions rely on. The first stage is joint funnel design, not executing a ready-made spec. We bring the ticketing specifics, you bring funnel and attribution expertise. Your approach to this stage is how we'll choose the contractor. Stages: (0) design the measurement architecture → (1) audit and repair what's already in place → (2) setup. Deadline — by the sales launch in August. After setup, on-call support only. Audience / who it's for B2C, buyers of tickets to cultural events. E-commerce experience is fine; the event specifics come from our side. How we'll evaluate you 1. Do you understand the task — state in your own words what matters most here. 2. How you'd approach the funnel-design stage. 3. Relevant experience: GA4, GTM, pixels, funnels, attribution, reading Meta metrics, email tracking. 4. Indicative rate / payment model. A detailed brief is attached. We'll finalize exact scope and estimate on a call.

Automation of data processing Execution of calculations according to specified algorithms, Automatic document generation Integration with 1C There are input data in one table that need to be automatically transferred to another, calculations performed according to certain rules, and ready documents generated for further automatic loading into 1C