Data Engineer

The RMR Group-posted 2 months ago
Orlando, FL
501-1,000 employees
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The Data Engineer will play a pivotal role in operationalizing the most-urgent data, analytics, artificial intelligence and machine learning initiatives for The RMR Group’s business initiatives. The bulk of the data engineer’s work would be building, managing, and optimizing data pipelines and then moving these data pipelines effectively into production for key data and analytics consumers. The Data Engineer must have experience in generative model training, data sampling techniques and natural language processing (NLP). You will be responsible for development and implementation of advanced analytical models and algorithms to support decision making and strategic planning within the organization. Data engineers also need to guarantee compliance with data governance and data security requirements while creating, improving and operationalizing these integrated and reusable data pipelines. This would enable faster data access, integrated data reuse and vastly improved time-to-solution for The RMR Group’s data and analytics initiatives. This role will require both creative and collaborative work with IT and the wider business. It will involve evangelizing effective data management practices and promoting a better understanding of data and analytics. The data engineer will also be tasked with working with key business stakeholders, IT experts and commercial real estate experts to plan and deliver optimal analytics and data science solutions.

  • Serve as a key contributor to identify, evaluate, and execute the development and implementation of data infrastructure.
  • Perform analysis on large datasets to make and implement recommendations for maximizing customer experience.
  • Assists in the design and implementation of relational databases and structures as needed.
  • Works collaboratively with Application development teams throughout the product development process, to ensure optimal usage of SQL.
  • 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.
  • Writes, refines, and optimizes T-SQL code for maximum performance, reliability, and maintainability.
  • Participate in developing cutting-edge storage design structures and data processing flows.
  • Creates documentation for both new and existing code.
  • Work collaboratively with varied stakeholders and business experts across departments.
  • Refine business data requirements for various data and analytics initiatives.
  • Participate in ensuring compliance and governance during data use.
  • Participate in logic and technical design, peer code reviews, unit testing, and documentation of code developed.
  • 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.
  • 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.
  • Demonstrated training in research methodology and empirical data analysis, including study design, statistical testing, and interpreting complex data patterns for real-world decision-making.
  • Strong foundation in statistical modeling, hypothesis testing, and experimental design.
  • Commercial real estate industry knowledge would be a plus.
  • 8+ years of experience in data engineering, data processing or including strategies for data ingestion, governance, storage, and retrieval.
  • 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.
  • Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management.
  • 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 experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional data integration technologies.
  • Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helps to move them in production.
  • Experience working with popular data discovery, analytics, and BI software tools like Power BI, Tableau, Alteryx, and others.
  • Experience with the Microsoft SQL Server Business Intelligence stack (SSAS, SSIS, SSRS), and Excel/Power Query.
  • 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.
  • Experience with agile and lean development methodologies (SCRUM/Lean).
  • Must be a self-starter with excellent problem-solving skills and excellent written/verbal communication skills.
  • Knowledge and experience with cloud data management and analytics with Microsoft Azure or Amazon AWS are strongly preferred.
  • Proven ability to develop, train, and evaluate machine learning and deep learning models.
  • Familiarity with transformer-based NLP architecture (e.g., BERT, GPT) and libraries such as Hugging Face and spaCy.
  • Proficient in causal inference, uplift modeling, and designing interpretable A/B experiments.
  • Deep understanding of experiment tracking and model reproducibility using MLflow, DVC, or Weights & Biases.
  • Strong business acumen with the ability to link models to measurable impact and decision-making.
  • Skilled in techniques for handling missing data, encoding categorical variables (e.g., one-hot, ordinal, frequency), and detecting outliers.
  • Experience with normalization and scaling strategies such as StandardScaler, Min-Max, log transformations, and robust scaling.
  • Familiarity with feature generation methods including binning, polynomial features, target encoding, and interaction terms.
  • Proficient with scikit-learn pipelines and feature-engine to enforce repeatability and modularity.
  • Ability to audit data for leakage, drift, and preprocessing-related errors during model training and inference.
  • Advanced Python programming for data science (pandas, scikit-learn, LightGBM, XGBoost, PyTorch, TensorFlow).
  • Strong SQL skills for exploratory data analysis and feature development across Snowflake, Synapse, or SQL Server.
  • Hands-on experience with Azure Machine Learning Studio: AutoML, compute clusters, deployment, and ML pipelines.
  • Data storytelling expertise using Streamlit, Plotly, Power BI, or other visual communication tools.
  • Version control and CI/CD familiarity using Git and integrated deployment tools.
  • Exposure to unstructured data (text, images, audio) and multimodal pipelines.
  • Awareness of privacy-preserving ML and responsible AI principles.
  • Experience analyzing production performance metrics and identifying model drift.
  • Excellent interpersonal and organizational skills.
  • Commercial real estate industry knowledge.
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