Data Specialist Job at hatch I.T., Somerville, MA

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  • hatch I.T.
  • Somerville, MA

Job Description

hatch I.T. is partnering with VIA to find a Data Specialist. See details below:

About the Role:

As a Data Specialist at VIA, you will play a pivotal role in the growth of their solutions. Your key responsibilities will include translating customer domain knowledge into data infrastructure requirements, designing and automating data ingestion and cleaning processes, transforming raw data into insightful visualizations, and ensuring the quality of our data-driven solutions. You will manage source documents to verify the accuracy of information, ensure data quality, and manage the incorporation of data into VIA’s work streams (database, modeling, and reporting decisions, to name a few). You will work on an Agile product delivery team that may include

developers, data and modeling specialists, and client delivery professionals.

About VIA:

VIA is making an impact, and so can you.

At VIA, their mission is to make communities cleaner, safer, and more equitable. They believe that by working across organizational boundaries, they can achieve greater collective good than they can individually. VIA overcomes digital barriers to collective action by providing the world’s most secure and simple data and identity protection solutions.

They are trusted by the U.S. Department of Defense and Fortune 100 companies around the globe to solve their toughest data and identity protection challenges. Using our Web3, quantum-resistant, passwordless technologies (19 issued patents), VIA protects data against theft, manipulation, and misuse.

Responsibilities:

  • Understand existing data ingestion and cleaning processes, and take responsibility for further refinement and implementation
  • Explore raw customer data to get a better understanding of the different files, columns, and characteristics (e.g., column averages, expected ranges, trends, standard deviations, etc.)
  • Understand customer needs and requirements, and make suggestions based on data
  • Work with VIA’s client delivery team and the customer to validate assumptions and resolve issues with the data
  • Own the ingestion of data into relational and/or non-relational databases standardized across customers, enabling data to connect to the rest of the data science pipeline
  • Leverage VIA libraries to ingest raw data from customers
  • Perform cleaning of raw data
  • Design end-to-end data pipelines
  • Coordinate with internal stakeholders and customers when information is missing or discrepancies are found
  • Perform quality control, identifying errors or discrepancies in data and data products, including automated and manual testing
  • Translate customer domain knowledge to create automated analyses and insights
  • Document assumptions and actions made during the data cleaning and wrangling process to provide traceability
  • Utilize data visualization tools to analyze data and craft compelling visual representations
  • Contribute to the continual improvement of internal tools for data cleaning and data quality assessment by identifying key data-related challenges that are ideal candidates for automation
  • Contribute to the delivery of data-based products to external customers, including data visualizations, data quality reports, and statistical analysis

Requirements:

  • 2+ years of experience in a data-driven role or equivalent in data-related research projects
  • Bachelor’s or Master's degree in science, mathematics, engineering, or a data-driven field
  • Competence in at least two of the following technologies:
  • Database technologies (e.g., SQL, PostgreSQL)
  • Python, R, or equivalent programming with data science libraries (e.g., NumPy, pandas)
  • Data visualization tools (e.g.,Plotly, seaborn, ggplot)
  • Ability to break down complex problems into actionable steps
  • Passionate about data quality
  • A self-starter attitude and demonstrated ability to learn new technologies quickly
  • Experience in the following is a plus:
  • Data dashboarding tools (e.g., Streamlit, Tableau, Dash)
  • Data pipelining (e.g., ETL workflows and tools)

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