VIA
Computer Software
DataSpecialist
“Data Specialist at VIA. Skills: Data pipeline design and implementation, AI-powered data products, Data integrity, Translating complex data findings into clear, compelling narratives. 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”
What You'll Achieve.
Turning raw, complex data into the trusted, AI-enhanced intelligence that powers VIA’s data products; Empowers our customers to make high-impact decisions where precision, security, and clarity are non-negotiable; Transforming raw inputs into high quality, trusted datasets; Deliver data-based products to external customers
Industry & Context.
Creative problem solvers; Resolve data-related issues; Decompose complex operational workflows into clear, repeatable steps
What They're Looking For.
Must Have
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, PostgreSQL), Data science libraries (e.g., NumPy, 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, 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
Nice to Have
Generative AI tools (e.g. AWS Bedrock, LangChain), Testing frameworks (e.g. pytest)
What You'll Do.
Understand the data and the domain
Partner with VIA's client delivery team and customers to translate domain knowledge into data infrastructure requirements
and resolve data-related issues
Explore raw customer data to build a clear picture of files
and characteristics (e.g. averages
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
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
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
How You'll Work.
Team & Collaboration
Operating on a high-velocity Agile team with developers, data and modeling specialists, and client delivery professionals; Coordinate with internal stakeholders and customers when information is missing or discrepancies are found; Team players who thrive in collaborative environments and celebrate the success of others
Communication Scope
Ability to translate complex data findings into clear, compelling narratives; communication capability to decompose complex operational workflows into clear, repeatable steps that both teammates and AI tools can act on; Clear stories
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