Senior Data Engineer

Netrio is a leading MSP in North America, specializing in IT solutions for small- to mid-market enterprises. We serve over 1,000 clients across industries with services including managed IT, cybersecurity, cloud, connectivity, voice, and custom application development.

We are seeking a Senior Data Engineer to design, build, and optimize modern data platforms that enable analytics, reporting, operational intelligence, and AI-driven solutions.

In this role, you will work across the full data lifecycle, developing scalable data pipelines, integrating complex data sources, and delivering high-quality data solutions that support business and customer objectives. You will collaborate with architects, engineers, analysts, and stakeholders to modernize data environments and implement cloud-based data platforms.

The ideal candidate is both a strong technical practitioner and a problem solver who enjoys working with modern data technologies, navigating ambiguity, and delivering scalable solutions in dynamic environments.

• →Serve as the technical leader for our data engineering initiatives.
• →Design, develop, and maintain scalable data pipelines that ingest, transform, and deliver data from structured, semi-structured, and unstructured sources.
• →Build and support ETL/ELT solutions across cloud-based data platforms, data warehouses, lakehouses, and analytics environments.
• →Develop data integration frameworks that enable reliable, secure, and efficient movement of data across systems.
• →Design and implement dimensional models, transformation logic, and data structures that support analytics, reporting, and AI initiatives.
• →Work with modern cloud data technologies such as Microsoft Fabric, Databricks, Snowflake, and related platforms.
• →Collaborate with architects and stakeholders to translate business requirements into scalable technical solutions.
• →Apply data quality, testing, monitoring, lineage, and governance best practices throughout the development lifecycle.
• →Optimize data pipelines and data platforms for performance, scalability, reliability, and cost efficiency.
• →Support modernization initiatives involving legacy data platforms, data warehouses, and integration technologies.
• →Implement modern engineering practices including source control, automated testing, deployment automation, and CI/CD processes.
• →Troubleshoot and resolve complex technical issues across data platforms and integration environments.
• →Contribute to architecture reviews, solution design discussions, and continuous improvement initiatives.
• →Stay current with evolving technologies, industry trends, and emerging approaches within data engineering, analytics, and AI.

• 6+ years of experience in Data Engineering, Data Integration, Analytics Engineering, Software Engineering, or a related technical discipline.
• Strong experience designing, building, and supporting production-grade ETL/ELT pipelines and data integration solutions.
• Advanced SQL skills, including query optimization, performance tuning, and data modeling.
• Exposure to cloud platforms such as AWS, Azure, or GCP
• Solid experience developing data solutions using Python, Scala, Java, or similar programming languages.
• Experience working with cloud-based data platforms, data warehouses, lakehouses, and modern analytics environments.
• Experience with distributed data processing technologies such as Spark or equivalent frameworks.
• Hands-on experience with modern data platforms such as Microsoft Fabric, Databricks, Snowflake, BigQuery, Redshift, or similar technologies.
• Familiarity with Git-based development workflows and CI/CD practices.
• Understanding of data quality, governance, lineage, security, and observability concepts.
• Ability to communicate effectively with both technical and non-technical stakeholders.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to adapt to changing priorities, learn new technologies quickly, and succeed in fast-paced environments.

• Experience implementing solutions using Microsoft Fabric, Databricks and Snowflake in production environments.
• Experience with Databricks and modern lakehouse architectures.
• Experience with dbt or similar transformation frameworks.
• Experience modernizing enterprise data warehouses and legacy integration environments.
• Experience with Azure Data Services or equivalent cloud-native data technologies.
• Experience supporting machine learning, artificial intelligence, or advanced analytics initiatives.
• Familiarity with semantic modeling, analytics-ready data structures, and data product concepts.
• Experience implementing data governance, metadata management, or data lineage solutions.