Compunnel, Inc.
The Data Engineer plays a critical role in building and operationalizing high-quality, trusted data to support enterprise-wide data and analytics initiatives.
This role focuses on designing, building, optimizing, and deploying data pipelines using industry-standard tools and practices.
The Data Engineer ensures data accuracy, completeness, consistency, and quality so it can be reliably consumed by business analysts, data scientists, and other analytics users across the organization.
Key Responsibilities Architect, develop, and maintain scalable and reliable data pipelines. Build and operationalize data integration workflows in alignment with enterprise data standards. Support modernization of data management infrastructure to increase automation and efficiency in data integration and processing. Partner with data scientists and business analysts to refine data requirements and support analytics and reporting use cases. Ensure data quality by validating data accuracy, completeness, consistency, and business rule conformance. Participate in data governance initiatives, including reviewing and promoting curated datasets and analytics assets for governed reuse. Train and guide internal teams on data preparation, pipelining, and data consumption techniques. Communicate complex technical concepts and analytical results to both technical and non-technical stakeholders. Work effectively both independently and as part of cross-functional teams to meet aggressive timelines. Contribute to continuous improvement across data architecture, tooling, and delivery processes. Perform other related duties as assigned.
Required Qualifications Bachelor’s degree in Computer Science, Statistics, or a related field, or equivalent practical experience. 4+ years of experience in data management and data engineering disciplines, including data integration, modeling, optimization, and data quality management. 4+ years of experience collaborating with cross-functional teams and business stakeholders on data management or analytics initiatives. Experience with data preparation tools and database programming languages. Strong analytical, problem-solving, and communication skills. Ability to manage multiple tasks while working under tight deadlines.
Preferred Qualifications Master’s degree in Computer Science, Statistics, or a related field. Hands‑on experience with data testing and validation. Strong experience with data engineering tools and platforms such as AWS Glue, Athena, and other cloud-based services. Experience working with open‑source and commercial data science and analytics platforms. Working knowledge of Agile methodologies. Experience designing, building, and managing large‑scale data pipelines and architectures. Experience working with large, diverse datasets to deliver measurable business value. Familiarity with DevOps practices, including version control, automated builds, testing, and release management. Willingness and ability to learn new technologies and tools as needed.
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This role focuses on designing, building, optimizing, and deploying data pipelines using industry-standard tools and practices.
The Data Engineer ensures data accuracy, completeness, consistency, and quality so it can be reliably consumed by business analysts, data scientists, and other analytics users across the organization.
Key Responsibilities Architect, develop, and maintain scalable and reliable data pipelines. Build and operationalize data integration workflows in alignment with enterprise data standards. Support modernization of data management infrastructure to increase automation and efficiency in data integration and processing. Partner with data scientists and business analysts to refine data requirements and support analytics and reporting use cases. Ensure data quality by validating data accuracy, completeness, consistency, and business rule conformance. Participate in data governance initiatives, including reviewing and promoting curated datasets and analytics assets for governed reuse. Train and guide internal teams on data preparation, pipelining, and data consumption techniques. Communicate complex technical concepts and analytical results to both technical and non-technical stakeholders. Work effectively both independently and as part of cross-functional teams to meet aggressive timelines. Contribute to continuous improvement across data architecture, tooling, and delivery processes. Perform other related duties as assigned.
Required Qualifications Bachelor’s degree in Computer Science, Statistics, or a related field, or equivalent practical experience. 4+ years of experience in data management and data engineering disciplines, including data integration, modeling, optimization, and data quality management. 4+ years of experience collaborating with cross-functional teams and business stakeholders on data management or analytics initiatives. Experience with data preparation tools and database programming languages. Strong analytical, problem-solving, and communication skills. Ability to manage multiple tasks while working under tight deadlines.
Preferred Qualifications Master’s degree in Computer Science, Statistics, or a related field. Hands‑on experience with data testing and validation. Strong experience with data engineering tools and platforms such as AWS Glue, Athena, and other cloud-based services. Experience working with open‑source and commercial data science and analytics platforms. Working knowledge of Agile methodologies. Experience designing, building, and managing large‑scale data pipelines and architectures. Experience working with large, diverse datasets to deliver measurable business value. Familiarity with DevOps practices, including version control, automated builds, testing, and release management. Willingness and ability to learn new technologies and tools as needed.
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