Data Analytics Engineer

Somerville, Massachusetts, United States, Washington, District of Columbia, United States

VIA is the digital infrastructure company mission-critical organizations rely on when speed and security matter most. The Department of War, government agencies, and global enterprises depend on VIA to disrupt corruption, defend critical infrastructure, and deliver humanitarian aid. VIA’s products span agentic AI, zero-trust identity, quantum-resistant data, and offline stablecoin payments. Every human and agentic AI action is intentionally captured, attributed, and defensible.

Designed for sub-second speed and multi-decade defensibility.

An impressive mission requires an equally impressive Data Analytics Engineer.

As a Data Analytics Engineer at VIA, you'll play a pivotal role in the growth of our solutions. You'll turn raw, complex, and often ambiguous data into the clear, trusted narratives that power VIA's data products and guide our customers' decisions. Operating on a high-velocity Agile team, you'll work cross-functionally with software engineers, data peers, and client delivery professionals to lead data initiatives end to end, in an environment where precision, security, and clarity are non-negotiable.

In this 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 customer data to build a clear picture of contents and characteristics (e.g. averages, expected ranges, trends, standard deviations) and make suggestions for data cleaning and analysis

Build AI-powered data products

• 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
• Build AI into VIA's data products, for example through 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

Oversee data pipelines

• Own the design, quality, and reliability of ETL/ELT pipelines, including work built with AI assistance
• 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

Improve the platform

• Contribute to the continual improvement of internal tools for data cleaning, modeling and analytics, and data quality assessment by identifying key data-related challenges that are ideal candidates for automation and AI enhancement

What you will bring to this role:

• 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:
• 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)

What does it take to be a successful VIAneer? Let’s break it down, our VIAneers are:

• Self-motiva…