AWS Redshift Migration & Cost Optimization — North America Pharma Company

Migrated the entire data infrastructure — feeds, reports, and dashboards — from an on-premise data warehouse to AWS Redshift for one of the leading pharmaceutical companies in North America. The project required independently developing new SQL pipelines, rebuilding dashboards, and delivering tight quality-control loops — all while coordinating with business users, cross-functional teams, and external vendors. The engagement delivered approximately $1.5M in operational savings.

1. Develop new SQL pipelines for feed generation and QC

Designed and built SQL queries from scratch to replicate and improve upon legacy feed generation logic. Each query was paired with a corresponding quality-control check to catch schema drift, null rates, and row-count discrepancies before downstream consumption.

-- Example: Feed generation with built-in QC gate
WITH feed_raw AS (
    SELECT
        patient_id,
        drug_id,
        prescribed_date,
        dosage_mg
    FROM staging.prescriptions
    WHERE prescribed_date >= CURRENT_DATE - INTERVAL '90 days'
),
qc_check AS (
    SELECT
        COUNT(*) AS total_rows,
        COUNT(patient_id) AS non_null_patients,
        COUNT(DISTINCT drug_id) AS unique_drugs
    FROM feed_raw
)
SELECT * FROM feed_raw
WHERE (SELECT total_rows FROM qc_check) > 0
  AND (SELECT non_null_patients FROM qc_check) = (SELECT total_rows FROM qc_check);

2. Rebuild reports and dashboards on Redshift

Translated every existing report and dashboard into the new Redshift-native environment. Worked directly with end users to validate outputs and incorporated feedback iteratively before sign-off.

3. Coordinate UAT with stakeholders across teams

Ran a structured user acceptance testing cycle with key stakeholders. Ensured all migrated reports matched expected outputs and documented any edge cases for post-migration monitoring.

4. Deliver $1.5M in operational savings

The streamlined cloud architecture eliminated redundant on-premise infrastructure costs and reduced query runtimes significantly. Active coordination across business, technology, and vendor teams was critical to realizing these savings on schedule.