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.
Enhance cloud capabilities by developing and implementing cloud application patterns, automating cloud services using infrastructure as code tools such as CloudFormation and Terraform.
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.
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.
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:
Minimum of 10 years of experience in data analytics, with at least 5 years in a management role managing large-scale data analytics projects.
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.
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.
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.
