The Senior Data Engineer will play a pivotal role in operationalizing the most-urgent data and analytics initiatives for our clients initiatives. The bulk of the data engineer s work would be in 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 data and analytics initiatives.
This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting 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.
Build data pipelines with Microsoft SQL Server Business Intelligence stack including relational databases, data cubes (tabular/multidimensional), SQL Reporting, Power BI, and other tools as needed. Managed data pipelines consist of a series of stages through which data flows. These data pipelines must be created, maintained, and optimized as workloads move from development to production for specific use cases. Architecting, creating, and maintaining data pipelines will be the primary responsibility of the data engineer.
Drive automation through effective metadata management: The data engineer will be responsible for using innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable, and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management.
Collaborate across departments: The data engineer will need strong collaboration skills in order to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with business experts in refining their data requirements for various data and analytics initiatives and their data consumption requirements.
Educate and train: The data engineer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new data requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The data engineer will be required to train counterparts in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
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.
Become a data and analytics evangelist: The data engineer will be considered a blend of data and analytics evangelist, data guru and fixer. This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.
Participate in logic and technical design, peer code reviews, unit testing, and documentation of code developed.
Participate in agile development teams, including 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. 4+ years of experience developing C#/.Net, SQL, and API Design.
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 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. These should include ETL/ELT, data replication/CDC, message-oriented data movement, and API design.
Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping 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
Commercial real estate industry knowledge or previous experience would be a plus.
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 is strongly preferred.
Excellent interpersonal and organizational skills.
1000 Lafayette Blvd Bridgeport, CT, 06604