Product Owner (Big Data/ IOT/IIOT)

Skills

Architecture and design of data analytics platform solutions System Solution Architecture. Data pipeline architect and data solution design (synthesize diagrams models and documentation) descriptive real time prescriptive and predictive analytics using AI/ML) managed large data sets (Big Data Reduce SPARK HBase Hive Kafka ODBC and SQL query language) Big Data engineering pipelines Data Lake/Mart/Warehouse and Machine Learning/Advanced analytics use cases Gathers requirements and build roadmaps architectures and lead decision making process Process improvement (manual to automation) cybersecurity Problem solving. Accelera's cloud architecture Aure Cloud based development exeprience Databases IoT CI/CD Working with multiple stakeholders

Description

Expert in architecture and design of data analytics platform. Effectively and efficiently process, store, and make data available to analysts and other consumers. Works with key business stakeholders, IT experts and subject-matter experts to plan, design and deliver optimal analytics and data science solutions. Manages a team of data engineers.
Gathers requirements and build roadmaps, architectures and lead decision making process for tools selection to help the Product Teams achieve their goals.
Becomes a trusted partner, collaborates and aligns with the wider Organization and Enterprise Data Strategy.
Creates and maintains optimal data pipeline architecture.
Identifies, designs, and implements internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Works with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
Guarantees compliance with data governance and data security requirements while creating, improving, and operationalizing these integrated and reusable data pipelines, enabling faster data access, integrated data reuse and vastly improved time-to-solution for the organization's data and analytics initiatives.

Responsibilities

• Significant experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes
• Analyzing complex business systems, industry requirements, and/or data regulations
• A strong background in processing and managing large data sets
• Proven Architecture and design experience in Big Data open-source tools including but not limited to Java, Ma
• Reduce, SPARK, HBase, Hive, Kafka, ODBC and SQL query language
• Proven design and development experience in clustered compute cloud-based implementation experience
• Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
• Experience in building analytical solutions
• Experience in databases and data management
• Experience on Large scale IoT, Big Data and Analytics powered Digital Transformation programs
• Solution Architecture skills for Big Data engineering pipelines, Data Lake/Mart/Warehouse and Machine Learning/Advanced analytics use cases
• Data related Cloud services across storage and compute services. [Preferably in Azure Databricks, Azure Data factory, Azure Synapse analytics, Storage account/Containers, Cosmos DB, SQL Server etc.
• Developing and Provisioning Azure Infrastructure using ARM (Azure Resource Manager) templates and CMI architecture framework/guidelines
• Azure account management (Create & Manage Azure account, define User Roles/Groups, Create Policies/permissions, Design automated Build and Deploy, Define Release Process, DefineConfiguration Management process, Create and manage Code Pipeline and Automate Build and Deployment Process
• Knowledge on Networking, Designing of Virtual Private Networks and Subnets, ability to integrate VPCs by VPC Peering
• Ability to design application with high availability and Scalability using load balancers and Auto Scaling Groups, Designing Microservice based architecture, Dockerize/Containerize microservice applications, Building server-less application using Azure Automation and ACI, Implementing the right DB design – NoSQL/SQL as suited to the application.
• Exposure to device registrations, device certification lifecycle management capabilities
• Implementation of IOT hub for managing IOT devices (IoT and IIoT)