Senior Data Engineer (Snowflake)

Quick Facts

• Role: Senior Data Engineer focused on Snowflake

• Key work: Build and optimize a Snowflake-based data evaluation pipeline

• Domain: Financial data analytics and credit card transaction datasets

Description

You will build and optimize an evaluation pipeline in Snowflake to assess the accuracy and added value of new credit card transaction datasets. The pipeline compares company revenue estimates against reported financial results and must remain scalable, automated, and cost-efficient. You will take ownership of the solution end to end—from data processing and ground-truth collection to quality validation and ongoing dataset evaluations.

Responsibilities

• Build and maintain a Snowflake-based evaluation pipeline to measure dataset accuracy (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

• Optimize Snowflake performance and compute costs using warehouse sizing, clustering, materialisation, query optimization, and data pruning

• Develop automated processes to collect, map, validate, and maintain ground truth company revenue data

• Implement data quality checks, reconciliation rules, and monitoring to identify inconsistencies before downstream reporting

• Evaluate new datasets as they become available and investigate data quality issues

• Own the pipeline end to end and make technical decisions as project requirements evolve

• Build on existing prototypes and previous work to deliver a reliable, production-ready solution

Requirements

• 6+ years of Data Engineering experience with Snowflake as the primary data platform

• Proven hands-on experience optimizing Snowflake warehouses and storage for performance and cost efficiency in production environments

• Experience building and optimizing pipelines processing very large datasets, including full transaction-level histories

• Strong SQL and Python skills; ability to own data pipelines end to end

• Practical data quality engineering experience: validation, reconciliation, monitoring, and ongoing data reliability

• Ability to work partially within the U.S. time zone: during onboarding, calls up to 10:00 PM CET a few times a week; later up to 5/6/7 PM CET

Benefits

Not specified in the provided posting.

Working Hours

Availability between 5:00 PM and 10:00 PM CET is essential during onboarding; afterwards, standard hours end around 5/6/7 PM CET (depending on the day).