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Data Engineering Lead, Enterprise Data Engineering & Operations (Real Estate)

Posted 20 January 2020
SalaryUSD 150000 to USD 200000
LocationChicago
Job type Permanent
DisciplineBanking & Insurance, Venture Capital & Private Equity
ReferenceDB/DAL/RE/USA

Job Description

  • Delivering industry leading Digital projects
  • Opportunity to work for a world-renown organization
  • Competitive salary package

Our client is a Fortune 500 member and one of the largest real estate organizations in the world. As a part of their strategic business improvement, they are currently seeking for a talented Data Engineering Lead to be a part of their Digital team.

Key Responsibilities

  • At least 15 years of work experience in data management and engineering disciplines, including data integration, modelling, optimization, and data quality, and other areas directly relevant to data engineering responsibilities and tasks.
  • Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and multi-departmental data management and analytics initiative.
  • A global mindset and demonstrated the ability to influence and manage individuals, groups, and organizations from other parts of the world.   Prior experience working across regions is strongly preferred

Key Requirements

  • Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as [R, Python, Java, C++, Scala, others]
  • 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, PL/SQL for relational databases and certifications on upcoming NoSQL, Hadoop oriented databases like MongoDB, Cassandra 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, API design, and access, and upcoming data ingestion and integration technologies such as stream data integration, CEP, and data virtualization
  • Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production
  • Strong experience in working with both open-source and commercial message queuing technologies, stream data integration technologies and stream analytics technologies

 

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