Data Engineer with Snowflake

Acaisoft; Województwo mazowieckie; Remote; PLN 180,000 - 270,000; Full time

Job summary

Acaisoft is seeking a Senior Data Engineer to join a fast-growing software house and work with a leading U.S. consumer insights provider.

You will design and own the end-to-end Snowflake-based data evaluation pipeline, handling large-scale transaction histories, implementing quality checks, and optimizing performance and costs. Onboarding requires availability during CET evenings; later, standard CET hours apply.

Qualifications

• 3+ years of experience in Data Engineering
• Snowflake as primary data platform
• Experience optimising Snowflake warehouses and storage for performance and cost efficiency
• Experience building and optimising pipelines processing very large datasets, including full transaction-level histories
• SQL and Python skills, with the ability to own data pipelines end to end

Responsibilities

• Build and maintain a Snowflake-based evaluation pipeline to measure accuracy of new datasets
• Develop evaluation metrics (e.g., correlation and MAE) to compare estimates against reported results
• Optimize Snowflake performance and compute costs via warehouse sizing and query optimisation
• Automate collection, mapping, validation, and maintenance of ground truth revenue data
• Implement data quality checks and reconciliation rules to catch issues early
• Own the pipeline end-to-end and adapt implementation as requirements evolve
• Enhance prototypes into production-ready solutions

Skills

SQL

Python

Data pipelines

End-to-end ownership

Tools

Snowflake

Job description

O projekcie:

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).

Wymagania:

• 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: occasionally up to 7 PM CET)
• 3+ years of experience in Data Engineering, with Snowflake as a primary data platform.
• Proven hands‑on experience optimising Snowflake warehouses and storage for performance and cost efficiency in production environments.
• Experience building and optimising pipelines processing very large datasets, including full transaction‑level histories rather than samples.
• SQL and Python skills, with the ability to own data pipelines end to end.

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.

Codzienne zadania:

• 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.
• 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.