> Markdown version of [/jobs/ext/4385577-data-engineer-data-pipelines-modeling](https://www.wearedevelopers.com/jobs/ext/4385577-data-engineer-data-pipelines-modeling). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer (Data Pipelines & Modeling) - **Company:** Katalyst Talent Agency - **Location:** United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Github, Python (Programming Language), SAP CRM, SQL Databases, Google Cloud, Apache Spark, Database Migration, Information Technology, Data Management, Stream Processing, Data Pipelines, Jenkins - **Published:** October 6, 2026 - **Apply:** https://www.dice.com/job-detail/297e84d4-5838-4905-ae0d-6ad5282fd6db ## About the Role * Strong experience in data engineering and pipeline development. * Proficiency in Python / SQL. * Hands-on experience with Apache Spark or similar big data tools. * Strong understanding of ETL/ELT concepts and data warehousing. * bility to work independently and in cross-functional teams. * Bachelor's / Master's in Computer Science, IT, or related field. Good to Have: * Exposure to CI/CD tools like Jenkins or GitHub Actions. * Knowledge of cloud platforms (AWS / Azure / Google Cloud Platform). * Experience in healthcare or regulated environments. ## Description * Design and implement robust data ingestion pipelines from multiple sources (APIs, databases, files, streaming systems). * Support C4C offline database migration, ensuring data accuracy and consistency. * Integrate data from enterprise systems into centralized data platforms. * Design and implement data models for Workforce planning. * Service operations forecasting. * Develop optimized schemas for reporting and analytics. * Ensure data quality, integrity, and consistency across models.