Data Engineer -Health Care

Job description

Job Description About Persistent We are an AI-led, platform-driven Digital Engineering and Enterprise Modernization partner, combining deep technical expertise and industry experience to help our clients anticipate what's next. Our offerings and proven solutions create a unique competitive advantage for our clients by giving them the power to see beyond and rise above. We work with many industry-leading organizations across the world, including 20 Fortune 50 companies and 4 of the 5 top banks in both the US and India, and numerous innovators across the healthcare ecosystem. Our disruptor's mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We reported $1,654.4M FY26 revenue with 17.4% Y-o-Y growth. We have delivered 24 sequential quarters of growth with $436.0M in Q4 FY26 revenue, up 3.2% Q-o-Q and 16.2% Y-o-Y growth. Our 27,500+ global team members, located in 18 countries, have been instrumental in helping the market leaders transform their industries. We have been recognized as the Fastest Growing IT Services Brand Globally in the 2026 Brand Finance IT Services 25 Report. We named a Leader in the Everest Group Private Equity (PE) Services PEAK Matrix Assessment 2026 and Software Product Engineering PEAK Matrix Assessment 2026. About Position: We are seeking a highly motivated Data Engineer with 8 to 12 years of experience in Data Engineering, Data Warehousing, and Big Data platform development. The ideal candidate will have strong expertise in Azure, Databricks, SQL, Python, and Generative AI technologies, along with experience building scalable and reliable data platforms that power analytics, reporting, and AI/ML initiatives. • Role: Data Engineer -Health Care • Location: All Persistent Locations • Experience: 8 to 12 years • Job Type: Full-Time Employment What You'll Do: • Design, develop, and maintain scalable data pipelines and integration frameworks. • Build and optimize enterprise data warehouses, data lakes, and cloud-native data platforms. • Develop robust ETL/ELT processes using SQL, Python, Databricks, and Azure services. • Create scalable data solutions that support business intelligence, analytics, and AI/ML workloads. • Build and maintain data models to support reporting, operational dashboards, and advanced analytics. • Collaborate with business stakeholders to understand data requirements and transform them into technical solutions. • Support feature engineering and data preparation activities for Machine Learning and Generative AI use cases. • Ensure data quality, integrity, availability, security, and platform reliability. • Implement monitoring, automation, alerting, and operational support processes. • Optimize data processing jobs for performance, scalability, and cost efficiency. • Support cloud data platforms including Azure Data Factory, Synapse, Databricks, and Snowflake. • Participate in code reviews and ensure adherence to data engineering and development best practices. • Work with DevOps teams to implement CI/CD pipelines and Infrastructure as Code practices. • Ensure compliance with data governance, privacy, security, and regulatory standards. • Contribute to Agile delivery processes including sprint planning, reviews, and release activities. • Leverage enterprise-approved AI tools to improve productivity, code quality, documentation, and analytical workflows. • Design and maintain scalable data pipelines and enterprise data integration frameworks. • Build and optimize data warehouses, data lakes, and cloud-based data platforms. • Ensure high levels of data quality, availability, performance, and governance. • Partner with analytics, AI/ML, and business teams to deliver trusted data products. • Implement automation, monitoring, and best practices across enterprise data platforms. Expertise You'll Bring: • 8 to 12 years of experience in Data Engineering, Data Warehousing, or Big Data development. • Strong expertise in SQL and Python programming for data engineering and transformation workloads. • Hands-on experience building and supporting large-scale ETL/ELT pipelines. • Experience working with Azure cloud services and modern data platform architectures. • Strong expertise in Azure Databricks, Spark, and distributed data processing frameworks. • Experience designing and managing enterprise data lakes and cloud-based data warehouses. • Knowledge of data modeling, data governance, data quality management, and metadata management. • Experience supporting AI/ML initiatives through feature engineering and data pipeline development. • Working knowledge of Generative AI concepts, Large Language Models (LLMs), and AI-enabled applications. • Experience with Azure Data Factory, Synapse Analytics, Snowflake, or similar enterprise data platforms. • Familiarity with CI/CD pipelines, DevOps methodologies, Infrastructure as Code, and platform automation. • Experience working with large-scale structured and unstructured datasets. • Understanding of data security, privacy, compliance, and governance best practices. • Healthcare, payer/provider, claims, operations, or contact center domain experience is preferred. • Strong analytical, troubleshooting, problem-solving, and performance optimization skills. • Excellent communication and stakeholder management capabilities. • Ability to collaborate effectively with business, analytics, engineering, and AI/ML teams. • Strong ownership mindset with a focus on delivering business value through data. • Regular usage of enterprise-approved AI tools such as GitHub Copilot, Microsoft 365 Copilot, and approved Generative AI platforms. • Ability to utilize AI tools to improve coding productivity, documentation quality, data analysis, and engineering workflows. • Understanding of Large Language Mod