Execute Platform BSS strategy within their regionKey to this role is your ability to identify opportunities in domain where TechM platform solutions can be pitched inFormulate on the Platform/Product BSS Strategy with the Platform heads & Regional Business HeadsEnable on a license/professional service revenue recognition modelWorking on Analysing client requirementDelivering Platform/Product pitch, platform demos to customer teamNeed to work along with Platform/Product Managers to enable and envisage new platform capabilities thereby enabling new revenue streamsNeed to establish relationship with alliance partner in each geos to enable Alliance led sale of Platform/ProductReviewing the Platform marketing & training collateralsRespond to RFP/RFIs across the dedicated regionWorking on proactive proposals and customer engagementsRequired Qualifications5-10 Years of experience in the industry, with minimum 4 years of experience in the Digital BSS IndustryShould be able to work closely with the client partner & will be part of platform demonstrations to customerShould be able to engage with business organizations within a telcoShould have a good understanding of the BSS Domain, including understanding of the capabilities of key BSS vendorsProven ability of customer engagement in Tier-1 and Tier-2 telecom operatorsExcellent verbal and written communication skills.

We are seeking a talented Lead Data Engineer to join our team and play a pivotal role in transforming raw data into valuable insights. As a Data Engineer, you will design, develop, and maintain robust data pipelines and infrastructure to support our organization's analytics and decision-making processes, with team handling.Responsibilities:Data Pipeline Development: Build and maintain scalable data pipelines to extract, transform, and load (ETL) data from various sources (e.g., databases, APIs, files) into data warehouses or data lakes.Data Infrastructure: Design, implement, and manage data infrastructure components, including data warehouses, data lakes, and data marts.Data Quality: Ensure data quality by implementing data validation, cleansing, and standardization processes.Performance Optimization: Optimize data pipelines and infrastructure for performance and efficiency.Collaboration: Collaborate with data analysts, scientists, and business stakeholders to understand their data needs and translate them into technical requirements.Tool and Technology Selection: Evaluate and select appropriate data engineering tools and technologies (e.g., SQL, Python, Spark, Hadoop, cloud platforms).Documentation: Create and maintain clear and comprehensive documentation for data pipelines, infrastructure, and processes.(Immediate Joiners)