Sr Data Engineer

Nearshore Business Solutions; Bogotá; A distancia; COP 345.781.000 - 502.955.000; Jornada completa

Descripción de la vacante

Nearshore Business Solutions seeks a Senior Data Engineer (6+ years) to design and maintain Snowflake-based data pipelines, optimize performance and costs, and implement data quality checks. This role is remote (Latin America) with ability to align with U.S.

time zones and collaborate with distributed teams. You will build evaluation pipelines to compare new datasets with revenue reports, implement MAE and correlation metrics, and ensure production readiness of data solutions across large-scale

Formación

• 6+ years of professional experience in Data Engineering.
• Snowflake as a primary data platform and optimization in production environments.
• Strong SQL and Python skills for end-to-end pipelines.
• Experience processing very large datasets with full transaction histories.
• Practical data quality engineering including validation, reconciliation, monitoring.
• Ability to work within U.S. time zones with distributed teams.

Responsabilidades

• Build Snowflake-based evaluation pipelines to measure accuracy of new datasets.
• Implement statistical evaluation metrics (e.g., correlation, MAE) for revenue comparisons.
• Optimize Snowflake performance and costs via warehouse sizing and query optimization.
• Develop automated processes to collect, map, validate, and maintain revenue data ground truth.
• Implement data quality checks and reconciliation rules for reliable reporting.
• Investigate data quality issues and scale pipelines for large-scale histories.
• Own end-to-end data engineering lifecycle and adapt solutions to evolving requirements.

Conocimientos

SQL

Python

Data pipelines

Data quality

Production troubleshooting

Herramientas

Snowflake

dbt

Descripción del empleo

Job Title: Senior Data Engineer

Location: Remote - Latin America (Must be able to work within U.S. time zones)
Type of Contract: Full-Time | Remote | Contractor
Salary Range: Market Rates

Our client is seeking a skilled Senior Data Engineer with 6+ years of experience in data engineering and deep expertise in Snowflake optimization, large-scale data pipelines, and data quality engineering. You will play a key role in building and maintaining a production-ready evaluation pipeline to assess the accuracy and reliability of new datasets against reported company revenue. Your work will directly impact data quality, analytical accuracy, and the performance and cost efficiency of their data infrastructure.

Key Responsibilities

• Build and maintain Snowflake-based evaluation pipelines to measure the accuracy of new datasets independently and in combination with existing data sources.
• Implement statistical evaluation metrics, including correlation and mean absolute error (MAE), to compare estimated company revenue against reported results.
• Optimize Snowflake performance and compute costs through warehouse sizing, clustering, materialization strategies, 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 systems to identify inconsistencies and maintain reliable downstream reporting.
• Evaluate new datasets, investigate data quality issues, and build pipelines capable of processing large-scale, full transaction-level histories.
• Own the end-to-end data engineering lifecycle, making technical decisions, improving existing prototypes, and adapting the solution to evolving project requirements.

Must-Have Qualifications

• 6+ years of professional experience in Data Engineering, with Snowflake as a primary data platform.
• Proven hands-on experience optimizing Snowflake warehouses, storage, and query execution for performance and cost efficiency in production environments.
• Strong SQL and Python skills, with the ability to design, build, maintain, and troubleshoot end-to-end data pipelines.
• Experience building and optimizing pipelines that process very large datasets, including complete transaction-level histories rather than sampled data.
• Practical experience with data quality engineering, including data validation, reconciliation, monitoring, and ongoing reliability improvements.
• Demonstrated ability to troubleshoot complex data issues, investigate inconsistencies, and deliver reliable, production-ready data solutions.
• Ability to work within U.S. time zones and collaborate effectively with distributed teams.

Preferred Qualifications

• Experience working with financial, market, transaction-level, or time-series data.
• Familiarity with statistical evaluation metrics, including correlation and mean absolute error (MAE), and their application to data accuracy assessments.
• Experience integrating and maintaining third-party data feeds, including working with data licensing and data-sharing arrangements.
• Hands-on experience with dbt or similar data transformation frameworks in Snowflake environments.
• Experience developing ground truth datasets and comparing estimated values against reported or verified results.
• Experience taking existing prototypes or proof-of-concept solutions and evolving them into scalable, reliable production systems.