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Delivering customer 360 with data mesh at a leading pharma enterprise

client stories customer 360 data mesh data migration DBT pharma data
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Enabled a dbt-powered data mesh that eliminates potentially out-of-sync business logic.

Executed a zero-downtime, low-risk data migration with data lineage, alerting, and observability across 10+ pipelines.

Established a unified, monitored data environment that accelerates issue resolution and empowers domain teams with complete customer views.

Challenge

The client was a pharmaceutical company known for its focus on developing and manufacturing a wide range of products in various therapeutic areas, including eye care, central nervous system treatments, gastroenterology, and women’s health. The client’s serverless AWS framework was highly complex, metadata-driven, and dependent on numerous external data sources, making collaboration and experimentation difficult.

The company’s existing business analytics tools offered limited monitoring and visibility, creating blind spots in pipeline performance and slowing issue resolution. Duplicate business logic across teams and the absence of end-to-end data lineage hindered explainability, governance, and the ability to respond quickly to new business requirements.

Solution

Hakkoda migrated the client’s legacy framework to dbt Cloud, implementing a Data Mesh architecture that eliminated redundant business logic and enabled domain-driven ownership. This migration modernized more than 10 pipelines with full data lineage and monitoring, and was executed with zero downtime using Kubernetes Pods to scale external operations while minimizing risk.

The Hakkoda team also introduced a GitHub-based CI/CD process with enforced coding standards, real-time observability and monitoring via DataDog and PagerDuty, and automated alerting for proactive issue management.

The model

Technology Used:

Snowflake

dbt

PagerDuty

Datadog

Full time resources:

1 engagement manager

1 data engineer

1 data architect

Project duration:

3 months

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