Responsibilities:
- Architect and implement scalable and resilient data pipelines, ensuring efficient data ingestion, processing, and storage in a cloud environment.
- Coordinate cross-functional teams, including business leads, system owners, architects, engineers, and external vendors to deliver high impact solutions.
- Develop and enforce policies and procedures for data management, ensuring the integrity and confidentiality of sensitive data.
- Develop and implement the strategic vision for the data analytics platform, ensuring alignment with organizational goals and objectives.
- Drive innovation by researching and proposing new tools, methodologies, and frameworks to enhance data analytics capabilities.
- Enhance cloud capabilities by developing and implementing cloud application patterns, automating cloud services using infrastructure as code (IaC) tools such as CloudFormation and Terraform.
- Ensure compliance with industry standards, regulatory requirements, and best practices in cloud security, data governance, and access control.
- Foster a culture of continuous improvement, encouraging team members to develop new skills and approaches to problem-solving.
- Implement robust project management practices, including risk management, stakeholder engagement, and performance tracking.
- Lead the design, development, and maintenance of a robust cloud data platform architecture, leveraging Databricks Lakehouse, AI, and ML technologies.
- Manage a portfolio of data analytics projects, ensuring they are delivered on time, within scope, and budget.
- Monitor and audit cloud resources to ensure they adhere to security guardrails and best practices.
- Oversee the creation of blueprints, roadmaps, and reference architectures for the data analytics infrastructure and services.
- Oversee the integration of diverse data sources, ensuring data quality and consistency, and facilitate the transformation of data into actionable insights through AI and ML models.
- Provide expert guidance on data warehouse solutions, advanced analytics, data modeling, and the implementation of Databricks Lakehouse.
- Stay abreast of emerging technologies and trends in data analytics, cloud computing, AI, and ML.
- Utilize modern cloud application architectures, including microservices, containerization, and serverless computing, to optimize performance and cost-efficiency.
Skills:
- Conduct research and stay up-to-date with the latest advancements in GenAI, Azure, OpenAI, AWS, and Google GenAI technologies.
- Deep understanding of machine learning (MLOps), data visualization (e.g., Power BI, Tableau), and event-driven architecture.
- Design, develop, and implement GenAI solutions that integrate with Azure and OpenAI platforms.
- Experience in data modelling, data transformation, and statistical computing (e.g., R, Python).
- Experience with AWS services, such as Amazon SageMaker, AWS Lambda, and AWS AI/ML services.
- Experience with cloud-based deployment and scaling of GenAI applications on Azure, AWS, and Google Cloud.
- Expertise in cloud-native technologies and services, including microservices, serverless computing, and containerization (e.g., Docker, Kubernetes).
- Knowledge of Google Cloud services, including Google Cloud AI Platform, Google Cloud Functions, and Google Cloud AutoML.
- Proficiency in databases (e.g., Oracle, MS SQL, MySQL, Teradata, Databricks), data repositories (e.g., data lakes, data marts).
- Strong knowledge of cloud data platforms, including architecture design, data integration, and the implementation of scalable data pipelines.
Requirements:
- Ability to translate business requirements into technical solutions and manage delivery timelines.
- Ability to troubleshoot complex technical issues related to AI model deployment and cloud infrastructure.
- AWS Cloud Practitioner or AWS Architect certification.
- Databricks Certified Data Engineer Associate or Professional certificate a plus.
- Degree/Master's in Computer Science, Information Technology, Computer Engineering, or equivalent.
- Excellent program and project management skills, including the ability to prioritize tasks, manage resources, and meet deadlines.
- Experience coordinating cross-functional teams including developers, cloud engineers, and possibly data scientists.
- Experience interacting with analytics stakeholders, including clinicians, policy makers, and economists.
- Experience mentoring and providing technical guidance to developers and engineers.
- Familiarity with healthcare informatics and data governance in the healthcare sector.
- Knowledge of cloud security best practices, identity and access management, and data privacy considerations relevant to AI workloads.
- Minimum of 10 years of experience in data analytics, with at least 5 years in management role managing large-scale data analytics projects.
- Proficiency in cloud-native architectures and services on both Azure and AWS.
- Project Management Professional (PMP) or similar certification is highly desirable.
- Proven experience with AWS cloud services, including setting up and managing cloud infrastructure, and deep expertise in Databricks Lakehouse and AI/ML technologies.
- Strong interpersonal and communication skills, with the ability to engage and influence stakeholders at all levels.
- Strong leadership abilities with a track record of managing high performing teams and complex projects.
- Track record of leading end-to-end delivery of generative AI projects or AI-powered platforms.
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