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Data Engineer, Enterprise Data, Analytics and Innovation, Digital Engagement at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Engineer, Enterprise Data, Analytics and Innovation, Digital Engagement in the United States.. This role offers a unique opportunity to design, build, and maintain robust data infrastructure that drives innovation and analytics across enterprise systems. You will work closely with cross-functional teams to ensure data reliability, accessibility, and scalability while supporting advanced analytics and predictive insights. This position is ideal for engineers who thrive at the intersection of technology and innovation, working with modern data platforms and pipeline architectures. You will be instrumental in integrating diverse data sources, implementing best practices for governance and security, and enabling data-driven decision-making. The environment is collaborative, flexible, and supportive, providing professional growth opportunities while making a tangible impact on healthcare and engagement analytics.. . Accountabilities. . Design, build, and operate ETL and ELT pipelines using Python and SQL to maintain data quality and reliability.. . Manage data across Bronze, Silver, and Gold layers of the Medallion architecture, ensuring smooth ingestion from transactional systems.. . Develop schemas, tables, and views optimized for analytics, APIs, and product use cases while enforcing data governance, security, and compliance standards.. . Integrate third-party and product-generated data, ensuring normalization, harmonization, and accurate documentation.. . Collaborate with innovation, data science, and AI teams to prototype and productionize analytics, predictive models, and dashboards.. . Monitor performance, troubleshoot issues, and optimize pipelines for scalability, reliability, and efficiency.. . . . 5+ years of professional experience in data engineering, ETL, or related roles.. . Strong proficiency in Python and SQL for data engineering tasks.. . Hands-on experience with modern data platforms, lakehouse architecture, and Medallion design patterns.. . Familiarity with Spark/PySpark, workflow orchestration tools (Airflow, dbt), containers (Docker), and Git version control.. . Strong communication skills, collaborative mindset, and problem-solving orientation.. . Experience with data quality, observability, and pipeline documentation.. . Bonus skills: Databricks, Microsoft Azure ecosystem, Delta Lake, metadata management, healthcare or scientific data experience, and API-based data exposure.. . . Company Location: United States.