Senior Data Engineer, Research & Analytics

Link Logistics•New York, NY

8d•$175,000 - $250,000

About The Position

Link Logistics Real Estate (“Link”) is a leading operator of warehouses and business parks, specializing in last-mile logistics real estate. Established by Blackstone in 2019, the company connects consumption, technology, and the supply chain across its portfolio, which spans half a billion square feet. We leverage our scale, proprietary data and insights, and foundational focus on sustainability to drive success for our customers’ businesses and deliver value for our stakeholders. We put our people, customers, and communities first and find ways to make a conscious, positive impact where we live and work. Every day, we work to reinvent and lead our industry forward by thinking bigger and challenging the status quo. THE GROUP: Link’s research department is the largest and most successful research effort among Blackstone’s real estate portfolio companies. The group’s efforts began with using data science models to measure and predict market rents for all US industrial assets, models that are now being used by Link and Blackstone in key industrial budgeting and investment decision making processes. The team takes an interdisciplinary approach to supply chain, logistics, and real estate research that draws from a variety of quantitative and primary research methods. THE ROLE: We are seeking a Senior Data Engineer to own and modernize our research and analytics pipelines. You will partner closely with data scientists, researchers, and enterprise technology to deliver robust, transparent, and business-aligned data and ML workflows. In this role, you’ll design, automate, and maintain robust ETL/ELT workflows that integrate data from APIs, vendor feeds, and internal systems. You’ll apply advanced data cleaning, matching, and validation techniques and uphold software engineering best practices, including CI/CD and automated testing. The systems you create will ensure stakeholders have timely, accurate, and trusted data to make high-impact decisions. You’ll work in a modern cloud environment with the autonomy to shape best practices, improve infrastructure, and influence how data powers analytics and strategy across the business.

Requirements

5+ years of hands-on data engineering, analytics engineering, or ML engineering experience (preferably in investment, research, or analytics-driven organizations)
Demonstrated experience modernizing legacy or “ad hoc” analytics codebases
Strong grasp of MLOps principles: reproducibility, version control, workflow orchestration, basic CI/CD, and monitoring
Understanding of data quality, testing, and governance best practices
Excellent communication skills: ability to translate business requirements into technical specs, and vice versa
Proven ability to work cross-functionally and push back constructively when needed
Strong experience with Azure Databricks and Spark (PySpark)
Advanced SQL and Python skills for large-scale data transformation and automation
Experience with dbt for transformation and modeling
Familiarity with Snowflake and integrating data for downstream tools like Power BI

Nice To Haves

Experience building internal data tools or dashboards with Streamlit

Responsibilities

Pipeline Ownership: Develop, maintain, and document data ingestion, transformation, and ML model pipelines. Diagnose existing code and processes, separating intentional design from accidental or legacy artifacts.
Process Improvement: Identify and remediate technical debt. Propose and implement improvements in reliability, performance, and maintainability.
Automation: Automate model retraining, scoring, and delivery. Develop basic CI/CD and monitoring for analytics workflows.
Collaboration: Serve as the main technical liaison with enterprise tech. Translate business requirements into concrete technical specs. Advocate for research/data needs in cross-team discussions.
Project Definition and Accountability: Scope and specify data and engineering requirements for new analytics and research projects. Drive clarity on priorities, timelines, and “definitions of done.” Proactively identify ambiguities and communicate risks or blockers.
Stakeholder Management: Set expectations and maintain clear documentation.
Technical Leadership: Mentor team members on engineering best practices, and, as appropriate, participate in code review and informal training.
Self-Accountability: Hold both yourself and the team accountable for missed deadlines, shifting requirements, and delivery standards.