Acaisoft

Senior Data Engineer (Snowflake)

140 - 190 PLN/ godz./ godz.B2BB2B (netto)

SeniorZdalnie·WarszawaFull-time·B2B

#462504·Dodano 15 dni temu·4

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Python Snowflake AWS SQLAILLMs Databricks

Podsumowanie oferty

Build and optimise a data evaluation pipeline for a financial data analytics project, assessing the quality and added value of credit card transaction datasets. Own the pipeline end to end in Snowflake, from processing and ground truth collection to validation and ongoing dataset evaluations.

Wymagania

6+ years of experience in data engineering, with Snowflake as a primary data platform.
Hands-on experience optimising Snowflake warehouses and storage for performance and cost efficiency in production environments.
Experience building and optimising pipelines that process very large datasets, including full transaction-level histories rather than samples.
Strong SQL and Python skills, with the ability to own data pipelines end to end.
Practical experience with data quality engineering, including validation, reconciliation, monitoring, and ongoing data reliability.

Nice to have:

Experience working with financial, market, transaction-level, or time-series data.
Familiarity with accuracy and tracking metrics such as correlation and mean absolute error (MAE).
Experience integrating and maintaining third-party data feeds or working with data licensing and data-sharing arrangements.
Practice with dbt or similar data transformation frameworks on Snowflake.

Obowiązki

Build and maintain a Snowflake-based evaluation pipeline to measure the accuracy of new datasets, independently and in combination 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.
Own the pipeline end to end, making technical decisions and adapting the implementation as project requirements evolve.
Build on existing prototypes and previous work to deliver a reliable, production-ready solution.

Inne informacje

Availability between 5:00 PM and 10:00 PM CET is essential during onboarding; calls may run until 10:00 PM CET a few times a week. Afterwards, standard hours end around 5, 6, 7, or 8 PM CET, depending on the day.

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