Data Engineering Architect, Senior (INDG)

Bloomberg Industry GroupArlington, VA

About The Position

Responsible for data engineering, data architecture, integration, maintenance and support. Individuals in this position must be a self-directed professional, who will also bring leadership skills and quickly learn new technologies and programming languages. You are a data engineer who thrives in an integrated team working closely with stakeholders, data analysts, data scientists, and ML engineers. You want to participate in overall architecture and strategy for the deployment of our end-to-end data pipelines. You have experience designing, building, and maintaining end-to-end scalable data pipelines. You are looking to apply dimensional modeling techniques to design tables and views that map business processes into an enterprise data model.

Requirements

  • Hands-on development experience with SQL and an ETL tool
  • Advanced SQL capabilities
  • Knowledge of database design techniques
  • Experience working with extremely large data volumes
  • Strong understanding of data warehousing methodologies, ETL processing and dimensional data modeling
  • Programming experience in Python
  • Bachelor’s degree in Computer Science or a related discipline, or equivalent experience
  • A minimum of 8 years of experience working in information systems, including at least 5 years developing and maintaining corporate relational DBMS and its components
  • Experience must cover database and data modeling, designing component-based, open-system architecture, and complex client-server information systems
  • Experience designing, implementing, and supporting data pipelines and services in multi-platform distributed environments, utilizing Java, Python, and/or Scala with strong object-oriented programming skills

Nice To Haves

  • Knowledge of reporting tools such as Tableau or Power BI
  • Master’s degree preferred

Responsibilities

  • Work with senior business stakeholders to break down business problems and design analytics solutions
  • Provide technical leadership to drive best practices for data modeling, ETL, and data quality
  • Design and build data pipelines to enable decision making through analytics
  • Develop automated solutions to replace manual data processes
  • Refine and enhance reporting data models
  • Act as a champion for data quality by proactively identifying data quality issues and coordinating with system owners to resolve issues quickly
  • Build relationships with senior business leaders to gather requirements and evangelize data analytics best practices
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