Data Engineer - Investments

Company Overview:

Hillwood Investment Properties is a leader in acquiring and developing high-quality industrial properties with 307.1M SF acquired and developed across the U.S., Canada, the United Kingdom, and Europe. Hillwood pursues well-located, functional land in the path of progress and has one of the largest land banks with a capacity of over 129.2M SF for future development. Hillwood collaboratively builds successful partnerships with public and private landowners, as well as other developers, to execute and invest in a broad spectrum of industrial projects. As a privately held company, Hillwood possesses the depth of capital, market expertise, industry relationships, and a forward-thinking vision to buy and build industrial properties that meet evolving markets' logistics, distribution, and manufacturing demands.

For more information on Hillwood's latest industrial availabilities across the U.S., U.K., and E.U., visit Hillwood.com.

Position Summary:

Hillwood Investment Properties (HIP) is seeking a technically strong and analytically driven Data Engineer to join our London office and support our institutional real estate investment platform.

This role sits at the intersection of data engineering and investment strategy. The selected candidate will design and build the data infrastructure that supports underwriting, forecasting, scenario modeling, and portfolio analytics across the UK and European platform.

Working closely with investment professionals in both London, Europe and the United States the Data Engineer will identify workflow inefficiencies, develop scalable technical solutions, and help institutionalize data architecture across the business. This is a high-impact role with meaningful ownership over the evolution of the team’s analytics and data platform.

Success in this role requires strong attention to detail, disciplined data governance practices, and the ability to proactively communicate across cross-functional teams and geographies. The ideal candidate will be comfortable translating technical concepts for investment professionals, collaborating across time zones, and ensuring data integrity in a fast-paced, deal-driven environment.

The position is well suited for an early-career data engineer or strong STEM graduate who thrives in a rigorous investment setting, values precision and accountability, and is motivated to build production-grade data systems that directly inform capital allocation decisions.

Responsibilities:

Data Architecture & Pipeline Development:

• Design, build, and maintain scalable ETL/ELT pipelines supporting underwriting, forecasting, scenario modeling, and portfolio analytics.
• Develop robust data ingestion processes integrating financial models, property management systems, market data providers, internal databases, and third-party APIs.
• Implement batch and real-time processing frameworks to support investment decision-making.
• Optimize pipeline performance, reliability, and scalability to support growing datasets.

Data Warehousing & Modeling:

• Design and maintain structured data warehouses and lakehouse environments to support analytics and reporting.
• Develop optimized data models (fact/dimension schemas, financial reporting models, reconciliation layers) tailored to investment workflows.
• Create curated data layers enabling efficient dashboarding, BI tools, and ad hoc analysis.
• Continuously improve data storage architecture to reduce redundancy and enhance performance.

Data Governance, Quality & Controls:

• Establish and monitor data quality controls, validation checks, and reconciliation frameworks.
• Implement governance standards to ensure data consistency, integrity, and traceability across investment datasets.
• Partner with investment professionals to define data definitions, metrics, and standardized reporting methodologies.
• Document data pipelines, system architecture, and workflows to enhance transparency and institutional knowledge.

Business Partnership & Solution Design:

• Collaborate with analysts and senior investment professionals to translate business requirements into scalable technical solutions.
• Identify process inefficiencies within underwriting and portfolio management workflows and propose automation or system enhancements.
• Support advanced analytics, modeling, and scenario analysis initiatives.
• Serve as a technical advisor on data-related strategy and infrastructure decisions.

Continuous Improvement & Innovation:

• Evaluate and implement modern data engineering tools, frameworks, and best practices.
• Stay current on emerging technologies in cloud data platforms, streaming architectures, and financial data systems.
• Contribute to the long-term roadmap for evolving the firm’s data and analytics infrastructure.

Required Skills and Abilities:

• Strong proficiency in SQL and Python.
• Demonstrated experience building and maintaining scalable ETL/ELT pipelines.
• Experience designing data warehouses and optimized data models.
• Familiarity with cloud-based data platforms (e.g., Azure).
• Experience with enterprise data warehouses such as Snowflake, Redshift, BigQuery, or Databricks.
• Hands-on experience with data ingestion and streaming tools (Kafka, Airbyte, Debezium).
• Experience working with lakehouse table formats such as Iceberg, Delta Lake, or Hudi.
• Practical experience with distributed processing frameworks such as Spark, Flink, or Databricks.
• Familiarity with Git and version control systems (preferably Azure DevOps).
• Strong analytical thinking, performance optimization skills, and attention to data quality and reconciliation.
• Demonstrated interest in financial markets, real estate investment, or quantitative modeling.
• Ability to communicate technical concepts to non-technical stakeholders.

Education and Experience:

• Bachelor’s degree in physics, Engineering, Mathematics, Computer Science, or related STEM field.
• 1–3 years of experience as a Data Engineer or in a comparable technical role.
• Strong internship, research, or applied project experience in data engineering, distributed systems, or analytics infrastructure.

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