Client Summary

We are building a global investment firm with a strong reputation for innovation and stability. They manage a diverse portfolio across asset classes and markets in more than 37 countries, consistently driving value creation through data-driven insights and advanced technology.

What You'll Do

• Design and build robust data pipelines to ingest, clean, transform, and aggregate large

volumes of structured and unstructured data

• Enable analytics and AI use cases by ensuring datasets are reliable, accessible, and

efficiently modeled

• Collaborate with data scientists, analysts, and business units to deliver usable datasets

for portfolio management, risk, operations, and compliance

• Implement monitoring, logging, and alerting frameworks to maintain high data quality

and pipeline uptime

• Contribute to data governance practices, including documentation, lineage tracking, and

compliance with internal and regulatory requirements

• Optimize performance and scalability of pipelines to handle growing data volumes and

use cases across the investment lifecycle

What We're Looking For

• Proficiency in Python for data engineering tasks

• Strong knowledge of SQL and relational databases

• Experience with ETL frameworks (e.g., Airflow, Luigi, Spark)

• Familiarity with cloud platforms (AWS, Azure, or GCP) and data warehousing (Snowflake,

Redshift, BigQuery)

Data practices:

• Experience with data ingestion, transformation, and modeling

• Solid grasp of data quality, governance, and compliance principles

Analytics & visualization:

• Exposure to BI tools like Tableau/PowerBI