Senior Software Engineer, Lead Data Engineering & Analytics

Serves as the Subject Matter Expert on Data & Analytics Solutions. Develop and maintain ETL/ELT pipelines for ingesting data from various sources into our data warehouse. Develop and maintain data models to support our reporting and analysis needs. Collaborate with data architects, data analysts and data scientists to understand their data needs and ensure that the data infrastructure supports their requirements. Ensure data quality and integrity through data validation and testing. Implement and maintain security protocols to protect sensitive data. Manage and provide guidance for the data engineers supporting projects, enhancements, and break/fix efforts. Provides strategic feedback to vendors on service delivery and balances workload with vendor teams. Supporting People with Disabilities Ability to manage multiple stakeholders and communicate progress to the Data Leadership. 8+ years of hands-on experience working on implementing and operating data capabilities and cutting-edge data solutions, preferably in a cloud environment. Breadth of experience in technology capabilities that span the full life cycle of data management including data lakehouses, master/reference data management, data quality and analytics/AI ML is needed. Databricks Expert and SME, along with DE experience on AWS cloud Experience with leveraging Gen-AI based automation Solution Architect, with an ability to craft Enterprise level Data Engineering and Analytics Solutions. Ability to lead a team of highly enthusiast Data Engineers AWS cloud expert and architect. Ability to manage multiple stakeholders and communicate progress to the Data Leadership. Ability to craft and architect data solutions, automation pipelines to productionize solutions. Hands-on experience developing and delivering data, ETL solutions with some of the technologies like AWS data services (Glue, Redshift, Athena, lakeformation, etc.). Cloudera Data Platform, Tableau labs is a plus. Create and maintain optimal data pipeline architecture, assemble large, complex data sets that meet functional / non-functional business requirements. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. Strong programming skills in languages such as Python, PySpark, R, PyTorch, Pandas, Scala etc. Experience with SQL and database technologies such as MySQL, PostgreSQL, Presto, etc. Experience with cloud-based data technologies such as AWS, Azure, or GCP (Preferably strong in AWS) Strong analytical and problem-solving skills Excellent communication and collaboration skills Functional knowledge or prior experience in Lifesciences Research and Development domain is a plus Experience and expertise in establishing agile and product-oriented teams that work effectively with teams in US and other global BMS site. Initiates challenging opportunities that build strong capabilities for self and team Demonstrates a focus on improving processes, structures, and knowledge within the team. Leads in analyzing current states, deliver strong recommendations in understanding complexity in the environment, and the ability to execute to bring complex solutions to completion.​ AWS Data Engineering/Analytics certification is a plus.