Data architect with AI

2 weeks ago

Middletown, United States Avance Consulting Full time
About the Job:

We are looking for a Data Architect with substantial experience and expertise in Azure, Databricks, and Synapse to join our team. This role requires a deep understanding of data architecture and the ability to develop cutting-edge solutions that drive business success.

Qualifications
  • Experience: Minimum of 15+ years of experience in data architecture, with at least 5 years of hands-on experience with Azure, Databricks, and Synapse.
  • Technical Skills: Proficient in SQL, Python, and/or R. Strong knowledge of data warehousing, ETL processes, and AI/ML model development.
  • Certifications: Relevant certifications such as Azure Solutions Architect, Databricks Certified Data Engineer, or equivalent are highly desirable.
  • Soft Skills: Excellent problem-solving skills, strong communication abilities, and a collaborative mindset.
Key Responsibilities
  1. Architecture Design and Strategy
  • Develop comprehensive data architecture strategies and solutions using Azure, Databricks, and Synapse.
  • Design scalable and secure data pipelines, storage solutions, and AI models to support business objectives.
  1. Implementation and Development
  • Lead the implementation of data solutions, ensuring best practices in data management, transformation, and analysis.
  • Utilize Databricks for advanced analytics and AI model development, ensuring seamless integration with Synapse for data warehousing.
  1. Collaboration and Leadership
  • Collaborate with cross-functional teams including data scientists, data engineers, and business analysts to understand requirements and deliver solutions.
  • Provide technical leadership and mentorship to team members, fostering a culture of innovation and continuous improvement.
  1. Data Governance and Compliance
  • Ensure all data solutions comply with industry standards and regulatory requirements.
  • Implement robust data governance frameworks to maintain data quality, integrity, and security.
  1. Performance Optimization
  • Monitor and optimize the performance of data pipelines and AI models, ensuring efficient resource utilization.
  • Troubleshoot and resolve any issues related to data integration and processing.
  1. Innovation and Continuous Improvement
  • Stay updated with the latest trends and advancements in data architecture, AI, and cloud technologies.
  • Propose and implement innovative solutions to enhance data processing capabilities and AI model accuracy.
  1. Technical Documentation
  • Develop and maintain detailed technical documentation for all data architectures, processes, and AI models.
  • Ensure documentation is up-to-date and accessible to relevant stakeholders.
  1. Stakeholder Management
  • Engage with key stakeholders to understand business needs and translate them into technical requirements.
  • Communicate complex technical concepts to non-technical stakeholders effectively.
  1. Project Management
  • Manage multiple projects simultaneously, ensuring timely delivery and alignment with business goals.
  • Use project management tools and methodologies to track progress and manage risks.
  1. Vendor and Tool Evaluation
  • Evaluate and recommend tools, technologies, and vendors to enhance the data architecture and AI capabilities.
  • Conduct proof-of-concepts and pilot projects to assess new solutions and their fit for the organization.

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