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Harrison Street Asset Management ("HSAM") is a leading global alternative investment management firm focused on real estate, infrastructure, and credit strategies across North America and Europe. Headquartered in Chicago, Toronto, and London with offices across North America, Europe, Asia and the Middle East, the firm has over $108 billion in assets under management on behalf of institutional investors and registered investment advisors globally.  Founded on a culture of innovation, HSAM is an early mover in alternative real estate and mid-market infrastructure. Today, the firm’s global team, consisting of more than 520 professionals, is committed to creating long-term value through deep sector expertise and forward-thinking strategies.  Under direction of the Head of Data Analytics & Architecture, Global, the successful Data Engineer candidate will be responsible for designing, building, and maintaining the data infrastructure and systems that support data-driven applications and analytics within Harrison Street. The candidate must have a broad understanding of the entire data & analytics ecosystem of tools and technologies, with hands on experience across the core data domains of business intelligence, data architecture, data science, data engineering, and data visualization. The candidate will analyze the data needs of Harrison Street Asset Management and use their skills in coding to develop and maintain secure data platforms. The successful candidate will be responsible for transforming data into formats that can be more easily analyzed by citizen analysts and data scientists alike. They will do this by developing, maintaining, and testing infrastructures for data generation that enable others within HSAM to more easily access in a secure way the data needed to derive data-driven insights and do their jobs. The Data Engineer will be responsible for the collection, storage, processing, and transformation of large volumes of data to make it accessible, reliable, and usable for analysis and decision-making purposes. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The successful candidate must be self-directed and comfortable supporting the data needs of multiple teams and systems. The right candidate will be excited by the prospect of optimizing our company’s data to support our next generation of investments.