Build and maintain a Snowflake-based evaluation pipeline to measure the accuracy of new datasets, both independently and combined with existing data.
Implement evaluation metrics such as correlation and mean absolute error (MAE) to compare estimated company revenue against reported results.
Optimise Snowflake performance and compute costs through warehouse sizing, clustering, materialisation, query optimisation, and data pruning.
Develop automated processes for collecting, mapping, validating, and maintaining ground truth company revenue data.
Implement data quality checks, reconciliation rules, and monitoring to identify inconsistencies before they affect downstream reporting.
Evaluate new datasets as they become available and investigate data quality issues.
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O pozici / o projektu
Původní popisek.
Hi there! If you’re seeking a high-impact role within an ambitious and fast-growing software house, you might have just found your perfect match!
We are seeking a highly skilled and experienced Senior Data Engineer to join our team. You will be working with one of the biggest consumer insights providers in the U.S.
In this role, you will build and optimise a data evaluation pipeline in Snowflake for a financial data analytics project. The goal is to independently assess the quality and added value of new credit card transaction datasets by comparing company revenue estimates against reported financial results, while ensuring the process is scalable, cost-efficient, and automated.
You will take ownership of the pipeline end to end, from data processing and ground truth collection to quality validation and ongoing dataset evaluations.
Working hours: Availability between 5:00 PM and 10:00 PM CET is essential during onboarding; afterwards, standard hours end between 5 and 7 PM CET (depending on the day).
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