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The Data, Analytics & AI Architect actively participates in providing the overall direction of architecture across the entire data analytics ecosystem.
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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 companys strategic business plans.
- As a key member of the Data, Analytics & AI Architecture team, y ou will contribute to planning, design, and implementation of the enterprise data and analytics vision and strategy aligned with the companys strategic business plans.
- You will enable innovation and understand the business analytics trends that can create business value while decreasing time to market and reducing solution complexity.
- In this r ole you will act as an advisor for various complex projects and initiatives and taking full ownership of all necessary architectural artifacts, h igh-level designs, proof of concepts (POCs), and deployment support for small to medium scope data, analytics, and GenAI solutions. You will play a pivotal role in defining the building blocks for future-state architecture and creating a roadmap for realizing these goals.
- You will thrive on bringing innovative approaches validated by quality research, industry insights, and proof of concepts supporting the suggested technology or architecture solution blueprint. In addition to demonstrating proficiency and a deep understanding across the four key data and analytics verticals, you will have substantial experience with data management, data security, and data operations.
- Furthermore, you will possess robust expertise in ML/AI capabilities. This diverse knowledge will empower you to recommend breakthrough improvements across the entire data and analytics ecosystem, ensuring the integration of AI/ML capabilities to enhance analytics processes and outcomes.
Primary Duties & Responsibilities:
Architecture Leadership:
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Responsible for 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.
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Collaborate with business and solution architectures for less complex data and analytics initiatives to ensure we achieve enterprise data and analytics objectives while meeting the business use case requirements
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Experience guiding cross-functional teams and influencing senior stakeholders
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Analyzes current architectures to identify weaknesses and develop opportunities for improvements
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Ensure the completeness of technical requirements and functional architecture analysis for the design and implementation of data and analytics business solutions
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Define, document, and maintain architecture patterns
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Complete architecture solution blueprint for data and analytics initiatives
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Ability to define and architect interim architectures and strategy to ultimately align to end state
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Develop and present a variety of architectural artifacts and documentation
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Establishes standard architectural principles, patterns, and processes
Innovation & Emerging Tech :
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Participate in proof-of-concept initiatives to assess and analyze fit for purpose of potential solutions
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Participate in data and analytics capability maturity assessment leveraging insights to build foundational future state architecture and corresponding roadmaps
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Foster innovation by evaluating emerging technologies and recommending adoption where appropriate
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Strives to continuously increase the overall data and analytics architecture practice maturity
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Deep understanding of data platforms, cloud technologies (eg, Azure, AWS, GCP), data modeling, and analytics tools
AI/ML Integration:
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Model development and deployment using frameworks such as TensorFlow, PyTorch, or Scikit-learn
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Familiarity with cloud-based ML platforms, such as AWS SageMaker, Azure Foundry, or Google AI Platform, Mosaic AI
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Experience in utilizing tools like MLflow for managing the ML lifecycle, including experimentation, reproducibility, and deployment
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Knowledge of natural language processing (NLP) and/or reinforcement learning techniques as applicable to business use cases
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Ability to integrate advanced analytics and AI/ML models into existing data workflows, ensuring seamless access and usability
Skills & Knowledge:
Behavioral Skills:
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Organizational Agility
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Growth mindset
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Creative, Innovative thinking
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Customer Focus
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Learning Agility & Self Development
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Cross functional people management
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Stakeholder management
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Mentoring and coaching
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Problem solving
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Drives for results
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Verbal & written communication
Technical Skills:
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Data and Analytics computing methodologies
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Data and Analytics platforms, tools, and integration processes both on-prem and cloud
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Common integration patterns (batch, micro-batch, near real time, real time)
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Data Fabric framework
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Basic Programming skills in Python, SQL
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Data management and data security
Tools Knowledge:
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Data pipeline tools like ADF, IICS
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Data platforms: Snowflake, Databricks, BigQuery
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BI tools: Power BI, Tableau, Looker
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Cloud platforms: Azure, AWS, GCP
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Streaming/Event processing and pub/sub software
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AI/ML Tools like MLOps, Tensorflow, Mosaic AI, AWS Sagemaker, Pytorch
Experience & Educational Requirements:
Preferred Certifications:
- Advanced Data Analytics Certifications
- AI and ML Certifications
- SAS Statistical Business Analyst Professional Certification
Skills & Knowledge:
Behavioral Skills:
- Coaching and Mentoring
- Decision Making
- Impact and Influencing
- Leadership Skills
- Multitasking
- People Management
PlanningTechnical Skills:
- Advanced Data Visualization Techniques
- Advanced Statistical Analysis
- Big Data Analysis Tools and Techniques
- Data Governance
- Data Management
- Data Modelling
- Data Quality Assurance
- Machine Learning and AI Fundamentals
- Programming languages like SQL, R, Python
Tools Knowledge:
- Business Intelligence Software like Tableau, Power BI, Alteryx, QlikSenseData Visualization Tools
- Microsoft Office SuiteStatistical Analytics tools (SAS, SPSS3)
