Senior Data Engineer (Snowflake)
New
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AcaisoftFinancial data analytics
Workplace type: remote; Country code: PL, 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).ContractSenior
Salary140 - 190 PLN per hour
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Job Details
- Languages
- Pl C2, en C1
- Experience
- 6+ years of experience in Data Engineering, with Snowflake as a primary data platform.
- Required Skills
- AWSPythonSQLArtificial IntelligenceSnowflakedbtDatabricksLLM
Requirements
- Have 6+ years of experience in Data Engineering, with Snowflake as a primary data platform.
- Have hands-on experience optimizing Snowflake warehouses and storage for performance and cost efficiency in production.
- Have experience building and optimizing pipelines that process very large datasets, including full transaction-level histories.
- Have strong SQL and Python skills and the ability to own data pipelines end to end.
- Have practical data quality engineering experience, including validation, reconciliation, monitoring, and ongoing data reliability.
- Be available between 5:00 PM and 10:00 PM CET during onboarding; afterward, standard hours end around 5/6/7 PM CET depending on the day.
- Experience with financial, market, transaction-level, or time-series data is a plus.
- Familiarity with correlation and mean absolute error (MAE) metrics is a plus.
- Experience with third-party data feeds, data licensing, or data-sharing arrangements is a plus.
- Practice with dbt or similar data transformation frameworks on Snowflake is a plus.
Responsibilities
- Build and maintain a Snowflake-based pipeline to evaluate new datasets independently and in combination with existing data.
- Implement correlation and mean absolute error (MAE) metrics to compare estimated company revenue with reported results.
- Optimize Snowflake performance and compute costs through 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 they affect downstream reporting.
- Evaluate new datasets and investigate data quality issues.
- Own the pipeline end to end, make technical decisions, and adapt the implementation as project requirements evolve.
- Build on existing prototypes and previous work to deliver a production-ready solution.
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