Data Engineer

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.

Harrison Street requires candidates to have independent, long-term work authorization that does not require future sponsorship or reliance on a third-party immigration process. 

Responsibilities:

60%     Data Engineering, Data Architecture, and ETL

Designing, building, and maintaining scalable and efficient data pipelines to extract, transform, and load (ETL) data from various sources into Enterprise Data Platform (EDP).

Designing and implementing data marts, including dimensional modeling, schema design, and optimization techniques.

Integrating and consolidating data from diverse sources, such as databases, sftp’s, APIs, and streaming platforms, ensuring data quality, consistency, and integrity.

Creating and maintaining data models, defining data structures, relationships, and data storage requirements, using techniques like entity-relationship diagrams and data flow diagrams.

Developing data transformation processes, including data cleansing, normalization, aggregation, and enrichment, to prepare data for analytics and reporting.

Identifying and resolving performance bottlenecks in data processing and storage systems, optimizing query performance, and improving overall data pipeline efficiency.

Implementing data quality assurance processes, performing data validation, testing data pipelines, and resolving data quality issues.

Monitoring data pipelines, diagnosing and troubleshooting issues, performing system upgrades and maintenance tasks to ensure data reliability and availability.

Collaborating with cross-functional teams, including Business Liaisons, analysts, and software engineers, and documenting data engineering processes, workflows, and best practices.

Supports training of Harrison Street team members on how to properly organize findings and read data collected.

Partners with Technology Infrastructure and Support Operations team to identify, design, and implement internal process improvements: e.g., automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability.

Partners with Technology Infrastructure and Support Operations teams to build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Microsoft Fabric.

Understands how Harrison Street’s data platform integrates within the overall technical architecture.




20%     Data Visualization

Solid understanding of data visualization principles, best practices, and design aesthetics.

Develop, maintain, and manage advanced reporting, analytics, dashboards using Tableau and Power BI.

Perform data analysis and validations.

Ability to translate business requirements into technical solutions and effectively communicate with stakeholders.

Experience in data analysis and interpretation to drive actionable insights.




10%     Project Management

Work closely with stakeholders to define project goals, scope, and deliverables.

Create project plans, tasks, and timelines using Azure Dev Ops.

Monitor progress to ensure timely completion of milestones and deliverables.

Conduct meetings, provide progress reports, and manage expectations to ensure alignment and stakeholder satisfaction.

Develop risk mitigation and contingency plans to address potential setbacks.

Apply organizational and communication skills to ensure project objectives are met, and deliverables are of high quality within the defined constraints.

5%       Cybersecurity Collaboration

Collaborate with the Cybersecurity team to adhere to organizational security standards, secure coding practices, and compliance requirements.

Work jointly with Cybersecurity teams on authentication, authorization, identity management, and secure Enterprise Data Platform development.

5%       Miscellaneous

Stay current on emerging technologies for data architecture, data engineering, cloud services, security practices, and AI platform capabilities.

Document system configurations, platform architectures, processes, and operational procedures.

Participate in training, workshops, and internal knowledge-sharing sessions.

Perform other duties as assigned to support evolving business and technology needs.

Requirements:

Microsoft Certified – Fabric Data Engineer or Azure Data Engineer.
Experience using Tableau and/or Power BI.
Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
Experience building and optimizing Azure data Factory pipelines, architectures, and data sets.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Strong analytic skills related to working with unstructured datasets.
Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
Strong organizational skills.
Experience supporting and working with cross-functional teams in a dynamic environment.
5+ years of experience in a Data Engineer role. They should also have experience using the following software/tools:
Experience with Microsoft Fabric or Azure Data Factory.
Experience with relational SQL databases.
Experience with Azure cloud services.
Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
Able to compile and organize statistical information retrieved and present findings to management.
Experience working with private and sensitive personal information.
Confident in decision making and the ability to explain processes or choices as needed.
Strong computer skills and ability to use necessary databases and software.
Ability to complete milestones and work toward multiple deadlines simultaneously.
Proficient in data warehousing solutions and ETL tools.
Proficient in database design, data definition, data dictionary, and related concepts.
Understands Machine Learning.
Can leverage data APIs.
Understands app/dev & SDLC.
Knowledge of algorithms and data structures.
Must be able to evaluate technical problems and determine solutions.
Must have strong written and verbal communication skills.
Must be able to follow and apply established security policies, procedures, and standards.
Must be able to read and understand technical manuals and vendor documentation.
Must be able to manage multiple technical workstreams independently.
Must be able to maintain professional and effective working relations with supervisors and co-workers.
Must be able to work flexible hours, including weekends and evenings.
Must be able to learn new skills and technologies.