Data Engineer

Overview

The Data Engineer will play a pivotal role in operationalizing the most-urgent data and analytics 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. 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 Server, Snowflake, Azure/Fabric lakehouse for storage and transaction processing
• 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: 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.
• Participate in logic and technical design, peer code reviews, unit testing, and documentation of code developed.
• Participate in agile development ceremonies, and interacting with both business analysts and end-users to come up with well-performing and scalable solutions.

• 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.
• Commercial real estate industry knowledge would be a plus.
• 8+ years of experience in data engineering 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. The ability to work with both IT and business in integrating analytics and data science output into business processes and workflows.
• Strong experience with popular database programming languages including SQL for relational databases and knowledge of upcoming No
  
  SQL/Hadoop oriented databases like Mongo
  
  DB, 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. These should include…