IT Tech Analyst

Huntington National BankColumbus, OH
Hybrid

About The Position

Our Enterprise Data Engineering team is growing, and we are looking for an outstanding Data Engineer to join our team. The Data Engineer will play a pivotal role in building and operationalizing the minimally inclusive data necessary for the enterprise data and analytics initiatives following industry standard practices and tools. Our goal is to be the Best performing Regional Bank in America, and we need data and analytics to meet that goal. As a Data Engineer, you will play a pivotal role in assisting with building and operationalizing the minimally inclusive data necessary for the enterprise data and analytics initiatives following industry standard practices and tools. Data Engineers will also test data quality to ensure Huntington’s data conforms to business rules and is accurate, complete, consistent, and uniform. The Data Engineer primarily focuses on supporting the building, managing, and optimizing data pipelines and then moving these data pipelines effectively into production for key data and analytics consumers like business/data analysts, data scientists or any persona that needs curated data for data and analytics use cases across the enterprise.

Requirements

  • Bachelor's Degree
  • 1+ year of related experience or qualified candidates who successfully complete Huntington’s Technical Youth training or a part of the apprenticeship training program, in lieu of related experience
  • Experience with various Data Management architectures and processes
  • Ability to design, build and manage data pipelines for data Internal Use
  • Experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets
  • Experience working with large, heterogeneous datasets to extract business value
  • Experience in working with DevOps capabilities like version control, automated builds, testing and release management capabilities
  • Ability to communicate complex results to technical and non-technical audiences
  • Willingness and ability to learn new technologies on the job
  • Data mining and database (MySQL) experience
  • Data visualization experience (Tableau, Excel)
  • Good in testing processes, defect management
  • Data automation
  • Creating/analyzing simple to complex SQL
  • 2+ years of experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks
  • 2+ years of experience with database programming languages and data preparation tools
  • Hands on data testing experience

Nice To Haves

  • Financial Services background preferred
  • Experience with data engineering tooling (e.g., Glue, Landa, Athena, AWS)
  • Knowledge of BI software tools
  • Experience with open-source and commercial data science platforms
  • Learn or Agile methodology

Responsibilities

  • Assist with the architecting, creating, and maintaining of data pipelines
  • Assist with renovating the data management infrastructure to drive automation in data integration and management
  • Work in partnership with data science teams and with business analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements
  • Support the training of counterparts across the organization in data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases
  • Work with data governance teams and participate in vetting and promoting content created in the business and by data scientists to the curated data catalog for governed reuse
  • Assist in designing, building, and maintaining data quality framework to ensure that data sets are valid, accurate, complete, consistent, and uniform.
  • Automate analyses and authoring pipelines via SQL, Python, Tableau, etc.
  • Support engineering staff and management in investigations of failures, containment activities, continuous improvement initiatives, etc., in order to drive meaningful improvements to production quality and output
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