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Data Pipelines

Governed data pipelines for regulated industries

End-to-end ETL/ELT across clinical research and enterprise IoT - built with SQL, Python, dbt, Spark, Airflow, and Databricks on Azure, GCP, and AWS, and wrapped in the data-quality controls, lineage, and audit-readiness that regulated work demands.

Approach

Methodology

  1. 1

    Model the data and its contracts

    Design efficient data models and schemas, and translate business requirements into validated KPIs with explicit data-governance standards before any pipeline is built.

  2. 2

    Build validated ETL/ELT

    Engineer SQL and Python pipelines with dbt, Spark, and Databricks to ingest, clean, and standardise data from APIs, databases, and enterprise systems.

  3. 3

    Embed data-quality controls

    Author Data Validation Plans and SOPs and build data-quality monitoring frameworks, embedding validation logic and controls across SQL-based workflows for audit-readiness.

  4. 4

    Govern, review, and report

    Run independent code reviews for traceable, compliant pipelines and surface data-quality metrics in governed dashboards (SQL, Power BI, DAX).

In Practice

Code Snippets

transform.pypython
from pyspark.sql import functions as F


def standardise_events(df):
    """Clean and standardise raw events on Databricks before load."""
    return (
        df.dropDuplicates(["event_id"])
        .withColumn("ingested_at", F.current_timestamp())
        .filter(F.col("event_id").isNotNull())
    )
dq_test.sqlsql
-- Data-quality control: no nulls or duplicate keys reach the silver layer
SELECT event_id, COUNT(*) AS occurrences
FROM silver.events
GROUP BY event_id
HAVING COUNT(*) > 1 OR event_id IS NULL;
Results

Case Studies

Clinical-trial data engineering at Metronomia

Problem
Complex clinical-trial data needed reliable, auditable ETL under GxP, ICH-GCP, 21 CFR Part 11, and CDISC/SDTM, with full traceability for regulators.
Approach
Engineered end-to-end ETL pipelines and data models in SQL and Python, built data-quality monitoring frameworks, authored Data Validation Plans and SOPs, and conducted independent code reviews as a second-line control.
Result
Improved processing speed and reliability by ~30% and cut project turnaround by ~20% through standardised, dashboard-tracked data-quality processes.

Enterprise IoT BI at Nexxiot AG

Problem
An enterprise IoT / logistics business needed a trustworthy pipeline running all the way from SQL extraction to governed reporting.
Approach
Developed ETL pipelines to ingest, clean, and standardise data from APIs, databases, and enterprise systems; designed efficient data models; and authored advanced SQL and DAX to turn requirements into validated KPIs while defining data-governance standards and quality controls.
Result
A governed reporting layer with validated KPIs and a single, controlled source of truth for the business.