Agencies & Reporting Teams

Automation for agencies and reporting teams that need reliable delivery, recurring insight production, and less coordination drag.

Agency and reporting workflows break when campaign data lives in too many systems, recurring deliverables depend on manual preparation, and QA relies on memory instead of structure. The strongest setup reduces reporting friction while keeping account teams fast and consistent.

Typical friction points

Recurring client reports pulling data from multiple ad, analytics, and CRM sources without one dependable workflow.

Campaign operations and reporting QA depending on repetitive spreadsheet work and manual handoffs.

Account teams losing time to status chasing, version control issues, and last-minute formatting fixes before delivery.

Suggested system layers

Scheduled data aggregation, transformation, and report assembly across client accounts

Dashboard delivery workflows with QA checks, alerts, and exception handling

Internal task routing for account updates, approvals, and reporting deadlines

Recurring KPI summaries and client-ready exports that no longer depend on manual prep

Relevant proof direction: Relevant case-study direction: enterprise data pipeline and reporting automation that eliminated manual reporting across client accounts and improved delivery consistency.

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Relevant completed work

Matching case studies from the wider portfolio.

Each card below is selected for this route so buyers can see real completed work that reflects similar operating pressure, system complexity, and commercial outcomes.

Digital advertising agency environment

Enterprise Data Pipeline & Reporting Automation

Result achieved: Manual reporting was eliminated across client accounts.

UNIDEX replaced manual analyst reporting with warehouse pipelines, automated dashboard delivery, and validation logic across more than ten marketing and analytics platforms.

Stack

Python, SQL, PostgreSQL, ClickHouse, Greenplum, Apache Airflow, S3, Jenkins, Superset

Specialist

UNIDEX delivery team

Mad Devs / Enji.ai

ETL pipelines for scheduled client-company data exchange

Result achieved: Automated data exchange reduced manual reporting and made recurring processing more dependable.

The broader project record describes Airflow, S3, and PostgreSQL pipelines connecting four client companies so reporting and data movement could happen on a schedule instead of through manual prep.

Stack

Python, Airflow, S3, PostgreSQL

Specialist

Джениш Мурсидинов

Analytical system and Airflow migration

Result achieved: A large-scale reporting stack moved from fragmented scripts to a monitored warehouse and orchestration layer.

The broader materials show a DWH platform, data marts, ETL and ELT automation, API integrations, and Airflow migration work that offloaded recurring analytics tasks from other internal teams.

Stack

Python, SQL, PostgreSQL, ClickHouse, Airflow, REST API, Superset, Jenkins, Docker

Specialist

Кылымбек Солтонов