Data Engineering for Partner Reporting, Privacy Compliance, and Ad Metrics

The client is one of the largest telecommunications and media operators in the airline States, with a major advertising business. Two engagements cover two different operational layers: data engineering for advertising and customer-profile capacity across a fiber-deployment program.

The data team at the advertising business faced three converging demands: a high volume of partner reports to generate on tight cadences, state-by-state privacy compliance that varies by jurisdiction, and ad-metric calculations that require both accuracy and speed. The engineering challenge is producing each of these at scale while keeping the data substrate auditable for disputes and replays.

Taller's data engineers automated partner report exports, updated the processes to handle state-level privacy compliance, and built workflows to track ad metrics and impressions at the volumes the advertising business actually operates at. Ad-tech data engineering at this scale lives on Snowflake plus Python plus Airflow plus AWS. The architectural choices that distinguish a working pipeline from a fragile one are mostly about idempotency and replay: each partner report has to be reproducible from a specific snapshot of the data warehouse so disputes can be settled against a defensible source of truth; each privacy-compliance filter has to apply at the jurisdiction the data subject was in at the time of the event; each ad-metric calculation has to handle the long tail of edge cases without resorting to per-case business rules. The Airflow DAG topology is where the operational discipline lives.

The engagement delivered increased partner-reporting efficiency and streamlined privacy-compliance assurance across the patchwork of state-level requirements.

Ad-tech operations at the advertising business's scale are bound by reporting cadence and compliance discipline more than by any individual feature decision. A data substrate that absorbs partner-reporting volume, privacy variance, and ad-metric complexity in one operational picture is the precondition for the advertising business to scale without scaling the data team linearly with revenue.