Data Engineer

Overview

Design, build, and operationalize data pipelines and AI/ML models for CRE analytics.

Key Responsibilities

• data pipelines
• t-sql
• database design
• data storage
• data analysis
• data governance

Tasks

-Build data pipelines with Azure Data Factory (ADF) to feed Microsoft SQL Server Business Intelligence stack including relational databases, data cubes (tabular/multidimensional), SQL Reporting, Power BI, and other tools as needed. -Refine business data requirements for various data and analytics initiatives. -Writes, refines, and optimizes T-SQL code for maximum performance, reliability, and maintainability. -Participate in logic and technical design, peer code reviews, unit testing, and documentation of code developed. -Serve as a key contributor to identify, evaluate, and execute the development and implementation of data infrastructure. -Assists in the design and implementation of relational databases and structures as needed -Participate in developing cutting-edge storage design structures and data processing flows. -Perform analysis on large datasets to make and implement recommendations for maximizing customer experience. -Works collaboratively with Application development teams throughout the product development process, to ensure optimal usage of SQL. -Work collaboratively with varied stakeholders and business experts across departments. -Creates documentation for both new and existing code. -Participate in ensuring compliance and governance during data use: It will be the responsibility of the data engineer to ensure that the data users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives.

Requirements

• python
• sql
• azure
• power bi
• etl
• machine learning

What You Bring

-Data storytelling expertise using Streamlit, Plotly, Power BI, or other visual communication tools. -Must be a self-starter with excellent problem-solving skills and excellent written/verbal communication skills. -Exposure to unstructured data (text, images, audio) and multimodal pipelines. -Strong experience with popular database programming languages including SQL for relational databases and knowledge of upcoming NoSQL/Hadoop oriented databases like MongoDB, Cosmos DB, others for nonrelational databases. -Strong foundation in statistical modeling, hypothesis testing, and experimental design -Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional data integration technologies. These should include ETL/ELT, data replication/CDC, message-oriented data movement, and API design. -Commercial real estate industry knowledge would be a plus. -Strong business acumen with the ability to link models to measurable impact and decision-making -Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management. The ability to work with both IT and business in integrating analytics and data science output into business processes and workflows. -Ability to apply DevOps principles to data pipelines to improve the communication, integration, reuse, and automation of data flows between data managers and consumers across an organization. -Proficient in causal inference, uplift modeling, and designing interpretable A/B experiments -Bachelor's degree in Computer science, statistics, applied mathematics, data management, information systems, information science, or a related quantitative field or equivalent work experience is required. -Demonstrated training in research methodology and empirical data analysis, including study design, statistical testing, and interpreting complex data patterns for real-world decision-making -Experience with normalization and scaling strategies such as StandardScaler, Min-Max, log transformations, and robust scaling. -Skilled in techniques for handling missing data, encoding categorical variables (e.g., one-hot, ordinal, frequency), and detecting outliers. -Excellent interpersonal and organizational skills. -Version control and CI/CD familiarity using Git and integrated deployment tools. -Familiarity with transformer-based NLP architecture (e.g., BERT, GPT) and libraries such as Hugging Face and spaCy. -Advanced Python programming for data science (pandas, scikit-learn, LightGBM, XGBoost, PyTorch, TensorFlow). -Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helps to move them in production. -Familiarity with feature generation methods including binning, polynomial features, target encoding, and interaction terms. -8+ years of experience in data engineering, data processing or including strategies for data ingestion, governance, storage, and retrieval. -Awareness of privacy-preserving ML and responsible AI principles. -Hands-on experience with Azure Machine Learning Studio: AutoML, compute clusters, deployment, and ML pipelines. -Strong SQL skills for exploratory data analysis and feature development across Snowflake, Synapse, or SQL Server. -Experience with the Microsoft SQL Server Business Intelligence stack (SSAS, SSIS, SSRS), and Excel/Power Query. -Master’s or PhD in a natural science discipline (e.g. Statistics, Mathematics, Computer Science, Physics, Engineering, etc.), or a quantitative social science (e.g., Economics, Political Science, Psychology, Sociology) with strong statistics training preferred -5+ years of experience developing SQL/T-SQL including, Single-row and Multi-row functions, complex joins, Common Table Expressions (CTEs), Procedures, Packages, ETL jobs, and Data linages in ADF. -Proven ability to develop, train, and evaluate machine learning and deep learning models -Experience with agile and lean development methodologies (SCRUM/Lean). -Ability to audit data for leakage, drift, and preprocessing-related errors during model training and inference. -Proficient with scikit-learn pipelines and feature-engine to enforce repeatability and modularity. -Server, Snowflake, Azure/Fabric lakehouse for storage and transaction processing. -Knowledge and experience with cloud data management and analytics with Microsoft Azure or Amazon AWS are strongly preferred. -Deep understanding of experiment tracking and model reproducibility using MLflow, DVC, or Weights & Biases. -Experience analyzing production performance metrics and identifying model drift. -Experience working with popular data discovery, analytics, and BI software tools like Power BI, Tableau, Alteryx, and others.

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Benefits

-Health Savings Account (HSA) & Flexible Spending Plans (FSA) -Wide Array of Voluntary, Employee-Paid Benefits to choose from including Critical Illness & Accident Insurance, Identity Theft Protection & Pet Insurance -Vision Insurance -Dental Insurance -401(k) Plan with Employer Match -Health Insurance -Parental Leave -Matching Gift Program -Tuition Assistance -Life & Disability Insurance -Holidays, Vacation & Sick Time

About The Rmr Group

-Specializes in managing and investing in real estate properties. -Primarily focuses on commercial assets including office buildings, retail spaces, and residential properties. -Partners with institutional investors to deliver value through strategic acquisitions and management. -Known for repositioning and revitalizing underperforming assets to maximize long-term returns. -Has a strong reputation with deep industry expertise and long-standing relationships with tenants and investors. -Achievements include successful large-scale, mixed-use developments in major metropolitan areas.

Sector Specialisms