Senior Data Engineer, Research & Analytics

Link Logistics • New York, NY

92d • $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’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.

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.

Nice To Haves

• 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.
• Experience building internal data tools or dashboards with Streamlit.

Responsibilities

• Develop, maintain, and document data ingestion, transformation, and ML model pipelines.
• Diagnose existing code and processes, separating intentional design from accidental or legacy artifacts.
• Identify and remediate technical debt.
• Propose and implement improvements in reliability, performance, and maintainability.
• Automate model retraining, scoring, and delivery.
• Develop basic CI/CD and monitoring for analytics workflows.
• 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.
• 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.
• Set expectations and maintain clear documentation.
• Mentor team members on engineering best practices.
• Participate in code review and informal training.
• Hold both yourself and the team accountable for missed deadlines, shifting requirements, and delivery standards.

Benefits

• Health insurance coverage.
• Retirement savings plan.
• Paid holidays.
• Paid time off.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior