Link Logistics Corp
Senior Data Engineer, Research & Analytics
Link Logistics Corp, New York, New York, us, 10261
Senior Data Engineer, Research & Analytics
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Senior Data Engineer, Research & Analytics
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Link Logistics 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:
Links research department is the largest and most successful research effort among Blackstones real estate portfolio companies. The groups 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, youll design, automate, and maintain robust ETL/ELT workflows that integrate data from APIs, vendor feeds, and internal systems. Youll 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. Youll 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. 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. QUALIFICATIONS: Background 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 Technical Skills 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 Bonus: Experience building internal data tools or dashboards with Streamlit Tech Stack Azure Databricks, Spark (PySpark), SQL, Python, dbt, Snowflake, Streamlit, Power BI, Azure Data Services, CI/CD tools $175,000 - $250,000 represents the presently anticipated base compensation pay range for this position at Link. Actual pay may vary based on various factors, including but not limited to location and experience. Link provides a variety of benefits to employees, including health insurance coverage, retirement savings plan, paid holidays, paid time off. EEO Statement The Company is an equal opportunity employer. In accordance with applicable law, we prohibit discrimination against any applicant, employee, or other covered person based on any legally recognized basis, including, but not limited to: veteran status, uniformed servicemember status, race, color, caste, immigration status, religion, religious creed (including religious dress and grooming practices), sex, gender, gender expression, gender identity, marital status, sexual orientation, pregnancy (including childbirth, lactation or related medical conditions), age, national origin or ancestry, citizenship, physical or mental disability, genetic information (including testing and characteristics), protected leave status, domestic violence victim status, or any other consideration protected by federal, state or local law. We are committed to providing reasonable accommodations, if you need an accommodation to complete the application process, please email LinkRecruiting@linklogistics.com. #J-18808-Ljbffr
Join to apply for the
Senior Data Engineer, Research & Analytics
role at
Link Logistics 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:
Links research department is the largest and most successful research effort among Blackstones real estate portfolio companies. The groups 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, youll design, automate, and maintain robust ETL/ELT workflows that integrate data from APIs, vendor feeds, and internal systems. Youll 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. Youll 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. 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. QUALIFICATIONS: Background 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 Technical Skills 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 Bonus: Experience building internal data tools or dashboards with Streamlit Tech Stack Azure Databricks, Spark (PySpark), SQL, Python, dbt, Snowflake, Streamlit, Power BI, Azure Data Services, CI/CD tools $175,000 - $250,000 represents the presently anticipated base compensation pay range for this position at Link. Actual pay may vary based on various factors, including but not limited to location and experience. Link provides a variety of benefits to employees, including health insurance coverage, retirement savings plan, paid holidays, paid time off. EEO Statement The Company is an equal opportunity employer. In accordance with applicable law, we prohibit discrimination against any applicant, employee, or other covered person based on any legally recognized basis, including, but not limited to: veteran status, uniformed servicemember status, race, color, caste, immigration status, religion, religious creed (including religious dress and grooming practices), sex, gender, gender expression, gender identity, marital status, sexual orientation, pregnancy (including childbirth, lactation or related medical conditions), age, national origin or ancestry, citizenship, physical or mental disability, genetic information (including testing and characteristics), protected leave status, domestic violence victim status, or any other consideration protected by federal, state or local law. We are committed to providing reasonable accommodations, if you need an accommodation to complete the application process, please email LinkRecruiting@linklogistics.com. #J-18808-Ljbffr