Analytics Solution Architect - Project Delivery Architect Platform component
o SAP HANA & BODS
Requirement analysis & Data modelling
o Experience from Product Delivery/System Integration related processes and requirements (Examples):
Project Performance - Forecast, Project finances & profitability metrices
Job stage Pyramids
o Managing expectations on Solution Access time (Performance) and Backlog/Sprint plans
o Good presentation skills (Senior Mgmt presentations)
o Experience in leading integration of multiple data sources
of various types data - structured, unstructured, logs, etc.
through various methods - REST, SOAP, direct database, file transfer. BOT, etc.
o Lead/Scrum a team of ~2 developers based in Bangalore, to grow with increased number of requirements
Data Analyst/Data Engineer Data Engineer Description: Develop and deliver visualization dashboards/ data solutions to support business needs & requirements by creating and optimizing data pipelines and data models for analytical solution s data lake / data warehouses towards specific micro services. The data engineer will also have responsibility for all data ingress into data lake/DWH, maintain the correct content, format and integrities all through the lifetime of such data.
Acumen for business flow understanding and expertise in data preparation and pre-processing
SQL knowledge and experience working with relational databases, query authorising (SQL) as well as a variety of other databases/date-sources.
Data exploration tools like Tableau is preferred.
Big data tools like Hadoop, Spark, Kafka, etc.
Data and Model pipeline and workflow management tools.
Stream-processing systems like Storm, Spark-Streaming, etc.
Object-oriented/object function scripting languages like Python, Java, C , Scala, etc.
Structured Query Language (SQL)
R or Python-Statistical Programming
AI Architect Architect Architect and have oversight of the design operations, such as the solution deployment, distribution, KPI monitoring, debugging, maintenance.
AI Architect's responsibilities include operationalizing AI, mapping requirements to implementation, selecting the appropriate technologies, and evaluating non-functional attributes such as security, usability, and stability.
AI Architect closes this data-to-insight-to-action loop, which requires deep understanding of the applications and integration infrastructure environment.
Closely understands mapping IT process to AI process. Influencing and working with stakeholders to establish and accelerate AI adoption.
Mapping requirements to implementation - Analyzing, coordinating, prioritizing and optimizing requirements come from diverse stakeholders, such as line-of-business users, Data Scientists, analysts, and administrators. Ensures they are implemented within current constraints. The implementation should satisfy current requirements and support future needs without significant rework.
Selecting technology - The AI Architect is responsible for selecting appropriate technologies from the many open source, commercial on-premises, and cloud-based offerings available. Integrating a new generation of tools within the existing environment is also crucial to help ensure access to accurate and current data. Because technology in this domain is evolving rapidly, another important responsibility of the AI Architect is ensuring that components can be replaced with well-suited alternatives that do not require any adjustment or downtime.
Evaluating non-functional attributes - When selecting technologies and building the AI platform, the AI architect needs to consider not only the functional requirements, but also the non-functional attributes of platform quality such as security, usability, and stability. The AI Architect plans, designs, and monitors these key characteristics of the AI platform to help ensure that it complies with enterprise standards and that it performs adequately as additional AI solutions are implemented