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About the role
About TresVista
TresVista is a global enterprise offering a diversified portfolio of services that enables its clients to achieve resource optimization through leveraging an offshore capacity model. TresVista's services include investment diligence, industry research, valuation, fund administration, accounting, and data analytics. TresVista has more than 1,800 employees across offices in North America, Europe, and Asia, providing high-caliber support and operating leverage to over 1,000 clients across geographies and asset classes, including asset managers, advisors, corporates, and entrepreneurs. Overview This role involves data to solve business problems, building and maintaining the data infrastructure to answer questions and improve processes. The occupant shall help build our data workflows, adding value to our technology solution and building data based decisioning. They shall work closely with business users and data science team to develop data lake house, models and pipelines for research, reporting, and machine learning. Roles and Responsibilities Build Data Lake Build enterprise data lake components Model front end and backend data sources to help draw a more comprehensive picture of user flows Build data pipelines that clean, transform, and aggregate data from disparate sources Develop data Pipeline: Understand decisioning situation and map it to KPI/Data needs Develop models that can be used to make predictions and answer questions for the overall business Ensure the scalability, maintainability and user experience are considered when designing and deploying new solutions and services Build data pipelines to augment text analytics solutions Prerequisites Strong understanding of data engineering concepts, ETL. Understanding of machine learning fundamentals - Good to have Working experience on data acquisition, curation, catalogue registration, data product life cycle and usage pattern Two or more years of experience with Python, SQL, PySpark and data visualization/exploration tools Familiarity with the AWS ecosystem, specifically Redshift and RDS Ability to articulate solutions and process designs clearly Strong knowledge of data integration (ETL), data modelling, data quality (DQ), data governance, iPaaS (APIs, micro services
data engineering,python,aws, data modelling,
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