{"schemaVersion":"jobsearcher.job.v1","id":"0216c55f3c06a5388ca2090d","url":"https://jobsearcher.com/jobs/0216c55f3c06a5388ca2090d","canonicalUrl":"https://jobsearcher.com/jobs/0216c55f3c06a5388ca2090d","title":"Data Engineer Cloud Data Pipelines, Integration & Analytics Enablement","description":"Data Engineer — Cloud Data Pipelines, Integration & Analytics Enablement Johns Creek, GA (hybrid) 4+ Months ContractThe Data Engineer will support data engineering initiatives by building reliable cloud-based data pipelines, scalable data structures, and analytics-ready datasets. This role is responsible for integrating data from multiple internal sources, automating data ingestion and transformation, creating reusable data models, and enabling downstream analytics, dashboards, reporting, and GenAI-enabled applications. The role partners closely with data scientists, analysts, business stakeholders, and platform teams to ensure data is accessible, well-structured, documented, and optimized for decision-making within an AWS cloud environment.\r\nResponsibilities\r\nDesign, build, and maintain cloud-based data pipelines for structured, semi-structured, and unstructured data sources.\r\nDevelop automated data ingestion, transformation, and refresh workflows using AWS services including S3, Glue, Athena, Lambda, Step Functions, DynamoDB, relational databases, and Python.\r\nCreate curated datasets, reusable schemas, metadata tables, and data models that support analytics, reporting, dashboards, and application development.\r\nDesign data models that establish reliable relationships across business entities using identifiers, reference tables, and relational structures.\r\nDevelop SQL queries, views, and data access layers for recurring analytical and reporting requirements.\r\nPartner with data scientists and analysts to prepare trusted datasets for analytics, machine learning, GenAI workflows, dashboards, and prototype applications.\r\nImplement data quality checks, validation rules, exception handling, logging, monitoring, and operational controls for data pipelines.\r\nDocument data sources, transformations, refresh schedules, metadata, assumptions, and known limitations.\r\nSupport the migration of manual and file-based processes to scalable, automated cloud data pipelines.\r\nCollaborate with platform, infrastructure, and security teams to ensure compliance with enterprise standards for data access, governance, and operational reliability.\r\nQualifications\r\nBachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Engineering, Applied Mathematics, or a related technical field.\r\n5–8 years of experience in data engineering, analytics engineering, cloud data platforms, ETL/ELT development, database design, or data integration.\r\nHands-on experience building cloud data pipelines and data lake solutions using AWS services such as S3, Glue, Athena, Lambda, Step Functions, DynamoDB, and relational databases.\r\nStrong proficiency in PySpark, Python, and SQL for data extraction, transformation, validation, automation, and loading.\r\nExperience working with structured and semi-structured data sources including CSV, Excel, JSON, APIs, databases, and file-based data.\r\nExperience designing reusable data models, metadata structures, reference tables, and relational schemas.\r\nExperience creating analytics-ready datasets that support reporting, dashboards, and application development.\r\nKnowledge of data quality, pipeline monitoring, logging, validation, and operational best practices.\r\nStrong collaboration and communication skills with cross-functional technical and business teams.\r\nPreferred Qualifications\r\nMaster's degree in Data Engineering, Cloud Architecture, Analytics Engineering, Enterprise Data Platforms, or a related field.\r\nExperience designing scalable data architecture patterns and reusable enterprise data models.\r\nExperience supporting GenAI-enabled applications and analytics workflows.\r\nExperience improving operational reliability and scaling prototype data pipelines into production-ready data products.\r\nMetasys Technologies is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.","company":"Metasys Technologies","rawCompany":"metasys technologies","city":"Alpharetta","state":"GA","isRemote":false,"isActive":false,"createdAt":"2026-08-08T01:24:22.196Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Engineer Cloud Data Pipelines, Integration & Analytics Enablement","description":"Data Engineer — Cloud Data Pipelines, Integration & Analytics Enablement Johns Creek, GA (hybrid) 4+ Months ContractThe Data Engineer will support data engineering initiatives by building reliable cloud-based data pipelines, scalable data structures, and analytics-ready datasets. This role is responsible for integrating data from multiple internal sources, automating data ingestion and transformation, creating reusable data models, and enabling downstream analytics, dashboards, reporting, and GenAI-enabled applications. The role partners closely with data scientists, analysts, business stakeholders, and platform teams to ensure data is accessible, well-structured, documented, and optimized for decision-making within an AWS cloud environment.\r\nResponsibilities\r\nDesign, build, and maintain cloud-based data pipelines for structured, semi-structured, and unstructured data sources.\r\nDevelop automated data ingestion, transformation, and refresh workflows using AWS services including S3, Glue, Athena, Lambda, Step Functions, DynamoDB, relational databases, and Python.\r\nCreate curated datasets, reusable schemas, metadata tables, and data models that support analytics, reporting, dashboards, and application development.\r\nDesign data models that establish reliable relationships across business entities using identifiers, reference tables, and relational structures.\r\nDevelop SQL queries, views, and data access layers for recurring analytical and reporting requirements.\r\nPartner with data scientists and analysts to prepare trusted datasets for analytics, machine learning, GenAI workflows, dashboards, and prototype applications.\r\nImplement data quality checks, validation rules, exception handling, logging, monitoring, and operational controls for data pipelines.\r\nDocument data sources, transformations, refresh schedules, metadata, assumptions, and known limitations.\r\nSupport the migration of manual and file-based processes to scalable, automated cloud data pipelines.\r\nCollaborate with platform, infrastructure, and security teams to ensure compliance with enterprise standards for data access, governance, and operational reliability.\r\nQualifications\r\nBachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Engineering, Applied Mathematics, or a related technical field.\r\n5–8 years of experience in data engineering, analytics engineering, cloud data platforms, ETL/ELT development, database design, or data integration.\r\nHands-on experience building cloud data pipelines and data lake solutions using AWS services such as S3, Glue, Athena, Lambda, Step Functions, DynamoDB, and relational databases.\r\nStrong proficiency in PySpark, Python, and SQL for data extraction, transformation, validation, automation, and loading.\r\nExperience working with structured and semi-structured data sources including CSV, Excel, JSON, APIs, databases, and file-based data.\r\nExperience designing reusable data models, metadata structures, reference tables, and relational schemas.\r\nExperience creating analytics-ready datasets that support reporting, dashboards, and application development.\r\nKnowledge of data quality, pipeline monitoring, logging, validation, and operational best practices.\r\nStrong collaboration and communication skills with cross-functional technical and business teams.\r\nPreferred Qualifications\r\nMaster's degree in Data Engineering, Cloud Architecture, Analytics Engineering, Enterprise Data Platforms, or a related field.\r\nExperience designing scalable data architecture patterns and reusable enterprise data models.\r\nExperience supporting GenAI-enabled applications and analytics workflows.\r\nExperience improving operational reliability and scaling prototype data pipelines into production-ready data products.\r\nMetasys Technologies is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.","datePosted":"2026-08-08T01:24:22.196Z","dateModified":"2026-08-08T01:24:22.196Z","hiringOrganization":{"@type":"Organization","name":"Metasys Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Alpharetta","addressRegion":"GA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0216c55f3c06a5388ca2090d"},"url":"https://jobsearcher.com/jobs/0216c55f3c06a5388ca2090d"}}