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The Data, Analytics & AI Architect actively participates in providing the overall direction of architecture across the entire data analytics ecosystem. The role is responsible for defining and driving the strategic vision for enterprise data and AI systems. This position oversees the design and implementation of scalable, secure, and innovative data platforms and AI capabilities that empower business intelligence, advanced analytics, and machine learning across the organization. This role is critical in shaping the enterprise data and analytics vision and strategy, ensuring alignment with the company's strategic business plans.
Job Responsibility:
Developing architecture for technology including best practices and guiding principles, and design of new data and analytics technology
Partner through implementation and operational support to ensure technology is achieving desired business outcomes
Collaborate with business and solution architectures for less complex data and analytics initiatives
Analyze current architectures to identify weaknesses and develop opportunities for improvements
Ensure the completeness of technical requirements and functional architecture analysis for the design and implementation of data and analytics business solutions
Define, document, and maintain architecture patterns
Complete architecture solution blueprint for data and analytics initiatives
Ability to define and architect interim architectures and strategy to ultimately align to end state
Develop and present a variety of architectural artifacts and documentation
Establish standard architectural principles, patterns, and processes
Participate in proof-of-concept initiatives to assess and analyze fit for purpose of potential solutions
Participate in data and analytics capability maturity assessment leveraging insights to build foundational future state architecture and corresponding roadmaps
Foster innovation by evaluating emerging technologies and recommending adoption where appropriate
Strives to continuously increase the overall data and analytics architecture practice maturity
Model development and deployment using frameworks such as TensorFlow, PyTorch, or Scikit-learn
Integrate advanced analytics and AI/ML models into existing data workflows, ensuring seamless access and usability
Requirements:
Bachelor’s Degree in Statistics, Computer Science, Information Technology or any other related discipline or equivalent related experience
12+ years of directly-related or relevant experience, preferably in healthcare data analytics or data engineering
Data and Analytics computing methodologies
Data and Analytics platforms, tools, and integration processes both on-prem and cloud
Common integration patterns (batch, micro-batch, near real time, real time)
Data Fabric framework
Basic Programming skills in Python, SQL
Data management and data security
Data pipeline tools like ADF, IICS
Data platforms: Snowflake, Databricks, BigQuery
BI tools: Power BI, Tableau, Looker
Cloud platforms: Azure, AWS, GCP
Streaming/Event processing and pub/sub software
AI/ML Tools like MLOps, Tensorflow, Mosaic AI, AWS Sagemaker, Pytorch
Advanced Data Visualization Techniques
Advanced Statistical Analysis
Big Data Analysis Tools and Techniques
Data Governance
Data Modelling
Data Quality Assurance
Machine Learning and AI Fundamentals
Business Intelligence Software like Tableau, Power BI, Alteryx, QlikSense
Data Visualization Tools
Microsoft Office Suite
Statistical Analytics tools (SAS, SPSS3)
Organizational Agility
Growth mindset
Creative, Innovative thinking
Customer Focus
Learning Agility & Self Development
Cross functional people management
Stakeholder management
Mentoring and coaching
Problem solving
Drives for results
Verbal & written communication
Coaching and Mentoring
Decision Making
Impact and Influencing
Leadership Skills
Multitasking
People Management
Planning
Nice to have:
Preferred Certifications: Advanced Data Analytics Certifications
AI and ML Certifications
SAS Statistical Business Analyst Professional Certification