Data Specialist
Listed on 2026-06-04
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IT/Tech
Data Analyst, Data Scientist
VIA is making an impact, and so can you.
At VIA, our mission is to make communities cleaner, safer, and more equitable. We believe that by working across organizational boundaries, we can achieve greater collective good than we can individually. VIA overcomes digital barriers to collective action by providing the world’s most secure and simple data and identity protection solutions.
We 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.
As a Data Specialist at VIA, you’ll play a pivotal role in the growth of our solutions. You are turning raw, complex data into the trusted, AI-enhanced intelligence that powers VIA’s data products. Operating on a high-velocity Agile team with developers, data and modeling specialists, and client delivery professionals, your work directly empowers our customers to make high-impact decisions where precision, security, and clarity are non-negotiable.
Inthis role, you will:
Understand the data and the domain
- Partner with VIA’s client delivery team and customers to translate domain knowledge into data infrastructure requirements, validate assumptions, and resolve data-related issues
- Explore raw customer data to build a clear picture of files, columns, and characteristics (e.g. averages, expected ranges, trends, standard deviations) and make suggestions grounded in the data
- Design and implement end-to-end, AI-enhanced ETL/ELT pipelines — striving for maximum automation and self-healing — that move raw customer data into standardized relational and non-relational databases ready for the rest of the data science stack
- Coordinate with internal stakeholders and customers when information is missing or discrepancies are found
- Run quality control on data and data products through both automated tests and targeted manual review, and document the assumptions and decisions made along the way so the work stays traceable
- Build AI into VIA’s data products — automated insights, anomaly detection, AI-assisted data quality checks, and natural-language interfaces over operational data
- Evaluate the quality and reliability of AI/ML outputs against domain expectations, and design the human-in-the-loop checks that keep our data products trustworthy
- Deliver data-based products to external customers, including interactive data analysis and investigation platforms, data quality reports, statistical analysis, and visualizations that turn complex findings into clear stories
- 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 and AI enhancement
- 3+ 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 Python, R, or equivalent programming language
- Competence in at least two of the following technologies:
- Database technologies (e.g., SQL, Postgre
SQL) - Data science libraries (e.g., Num Py, pandas)
- Data pipelining workflows and tools (e.g., Dagster, Airflow, dbt)
- Cloud providers (e.g. AWS, Azure), including software development kits used to access data and services on these platforms
- Ability to translate complex data findings into clear, compelling narratives
- Strong communication capability to decompose complex operational workflows into clear, repeatable steps that both teammates and AI tools can act on
- Passionate about data integrity, with a proven track record of transforming raw inputs into high quality, trusted datasets
- A self-starter attitude and demonstrated ability to learn new technologies quickly
- Experience in the following is a plus:
- Generative AI tools (e.g. AWS Bedrock, Lang Chain)
- Testing frameworks (e.g. pytest)
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