ESSENTIAL DUTIES & RESPONSIBILITIES
Task
Work with business users and other subject matter experts to implement Data Analytics solutions
Collaborate with data engineers, BI developers, and product teams, to define requirements and deliver high-value data products
• Participate in Data and Analytics discovery to establish feasibility of new use cases
• Design or suggest improvements to optimize existing solutions
• Implement and test solutions to ensure results meet business criteria
• Deploy and enhance the solutions
• Run and Maintain Operations for Data and Analytics Solutions
Develop and maintain complex Data pipelines, ETL processes, reports and business dashboards
• Build and enhance Data Pipelines to ingest data
• Own and enhance ETL processes for multiple Data Warehouses
• Develop and maintain new reports and dashboards
• Develop and maintain CICD artifacts
• Monitor and troubleshoot Data pipelines and ETL processes
• Document new and existing processes
Automate and Self-Service enable Data Solutions
• Migrate existing legacy Datawarehouse and ETL processes to modern BI platforms and technologies
• Automate current manual processes across business functions
• Self-service enable Data Solutions to empower business with faster service and insights
Support and maintain Data and Analytics solutions
• Troubleshot and resolve production issues
• Implement enhancements and bug fixes
• Operationalize daily data extracts, pipelines, reports and Dashboards
• Proactively and independently identify performance issues and recommend enhancements
• Proactively monitor and support production systems, including on-call responsibilities
• Mentor and assist other team members on issue resolution and troubleshooting
Develop software with a security-first mindset using knowledge of standard security protocols and common security risks, secure coding techniques, and appropriate usage and protection of sensitive information such as PHI. Identify potential security issues during requirements analysis, development, and threat modeling discussions.
KNOWLEDGE & REQUIREMENTS
• Strong experience in Data Engineering, Modeling and Data Warehousing technologies
• Strong experience in ETL design, development, testing, maintenance, and implementation of data pipelines and analytics initiatives
• Strong SQL programming/python scripting/data analysis experience
• Strong experience in MS SQL Server, SSIS and SSRS Technologies
• Experience in AWS Cloud Data Services (AWS RedShift, and Glue)
• Experience in Sigma Computing BI tool and Qlik Replicate is a plus
• Experience in Databricks is a plus
• Experience in GenAI, LLM and Agentic AI is a plus
• Experience with React and other UI development tools is a plus.
• Experience and knowledge in Clinical Services and Healthcare industry is a strong plus
• Experience in gathering requirements, development, testing and maintenance of Data and Analytics Solutions
• Experience leveraging cloud native services, AI tools/services, and DevOps practices
• Provides a positive impact on team: influences team decisions, mentors peers, drives innovation
• Excellent written and verbal communication skills
• Experience with developing Chatbot automations is a plus
DESIRABLE QUALIFICATIONS
• Self-motivated, quick learner, and thrives in fast-paced environments while adapting seamlessly to new technologies
• Experience working with healthcare technology.
• Experience working with PHI data and HIPAA standards.
• Experience resolving issues that do not have clear answers.
• High sense of urgency and accountability
• Adaptable, friendly, and ability to work with teams.
• Passion for data and digging into the minutia of datasets.
EDUCATION & EXPERIENCE REQUIREMENTS
• Minimum years of work experience: 7 years
• Minimum level of education or education/experience: Bachelors or equivalent work experience in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, IT
• 4+ years of Data Warehousing experience
• 4+ years of experience in build data pipelines, ETL, and complex SQL scripting
• 4+ years of experience in Data Engineering
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