Data Visualization Engineer

C Mack Solutions LLCAshburn, VA
Onsite

About The Position

C. Mack Solutions is seeking a motivated Data Visualization Engineer with agile methodology experience to join our team and support a complex program to provide Agile development and operations and maintenance for critical systems on a mission-critical program for the Federal Government. You will be working with an established team to create meaningful dashboards that support our customer's mission-critical decision-making. The environment is complex, with large datasets both in depth and breadth. Your role will always include learning something new, whether a tool or a new data domain.

Requirements

  • Must live within the DC, MD, VA area.
  • Active CBP Public Trust Clearance.
  • Bachelor's degree plus 3+ years of overall technical experience, including analysis or development roles with databases, data integration, data mining, big data, data visualization.
  • At least 1 year developing dashboards using data visualization technologies, such as PowerBI, Qlik Sense, or Tableau.
  • Experience in data modeling, scripting, and ETL development within Qlik environments.
  • Strong SQL experience working with relational databases and large datasets.
  • Good communication skills, particularly in requirements definition and documentation of visualization artifacts.
  • Ability to learn new tools quickly as needed to provide new ideas for solving problems.
  • Ability and desire to work with other program staff and customers to reach design decisions within given constraints.
  • Excellent diplomacy and communication skills with both clients and technical staff.

Nice To Haves

  • Demonstrated project experience in data visualization and analysis using Qlik Sense specifically, highly preferred.
  • Competence in Python, Spark, and PySpark.
  • Experience working with federal government clients.
  • Experience with AWS cloud services.

Responsibilities

  • Create meaningful dashboards that support mission-critical decision-making.
  • Work with large datasets both in depth and breadth.
  • Learn new tools and new data domains.
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